[SYCL] support the missed types in cpy (#26005 )

* support the missed types in cpy * use correct funct * rm unused code
llama : enforce the same K and V cache types for DeepSeek V4; enable FA if V cache is quantized (#25871 )
2026-08-01 00:58:01 +02:00 · 2026-07-31 10:25:16 +03:00 · 2026-07-31 10:03:30 +03:00 · 2026-07-31 09:40:52 +03:00 · 2026-07-31 09:20:28 +03:00 · 2026-07-31 09:19:41 +03:00
67 changed files with 1780 additions and 919 deletions
@@ -2,65 +2,56 @@

 ![llama](https://raw.githubusercontent.com/ggml-org/llama.brand/refs/heads/master/cover/llama-cpp/cover-llama-cpp-dark.svg)

+<div align="center">
+
+<b>LLM inference in C/C++</b>
+
 [![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://opensource.org/licenses/MIT)
 [![Release](https://img.shields.io/github/v/release/ggml-org/llama.cpp)](https://github.com/ggml-org/llama.cpp/releases)
 [![Server](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/server.yml)
 [![Docker](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/docker.yml)
 [![Winget](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml/badge.svg)](https://github.com/ggml-org/llama.cpp/actions/workflows/winget.yml)

-[Manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc)
+[manifesto](https://github.com/ggml-org/llama.cpp/discussions/205) / [ggml](https://github.com/ggml-org/ggml) / [ops](https://github.com/ggml-org/llama.cpp/blob/master/docs/ops.md) / [maintainer PRs](https://github.com/ggml-org/llama.cpp/issues?q=is%3Apr%20is%3Aopen%20draft%3AFalse%20(author%3Argerganov%20OR%20author%3AKitaitiMakoto%20OR%20author%3Adanbev%20OR%20author%3Aaldehir%20OR%20author%3Amax-krasnyansky%20OR%20author%3ACISC%20OR%20author%3Aggerganov%20OR%20author%3Aam17an%20OR%20author%3Abartowski1182%20OR%20author%3Ahipudding%20OR%20author%3AServeurpersoCom%20OR%20author%3Apwilkin%20OR%20author%3Areeselevine%20OR%20author%3Angxson%20OR%20author%3Ajeffbolznv%20OR%20author%3A0cc4m%20OR%20author%3Aangt%20OR%20author%3AIMbackK%20OR%20author%3Aarthw%20OR%20author%3AJohannesGaessler%20OR%20author%3AORippler%20OR%20author%3Aruixiang63%20OR%20author%3Axctan%20OR%20author%3Aallozaur%20OR%20author%3Ayomaytk%20OR%20author%3Aaendk%20OR%20author%3Agaugarg-nv%20OR%20author%3Ataronaeo%20OR%20author%3Aforforever73%20OR%20author%3Alhez%20OR%20author%3Anetrunnereve%20OR%20author%3Afairydreaming)%20sort%3Aupdated-desc) / [dev branches](https://github.com/ggml-org/llama.cpp-dev/blob/master/README-features.md) / [compile times](https://github.com/ggml-org/llama.cpp-dev/blob/master/README-compile-times.md) / [lib llama API](https://github.com/ggml-org/llama.cpp/issues/9289) / [llama-server REST API](https://github.com/ggml-org/llama.cpp/issues/9291)

-LLM inference in C/C++
-
-## Recent API changes
-
- [Changelog for `libllama` API](https://github.com/ggml-org/llama.cpp/issues/9289)
- [Changelog for `llama-server` REST API](https://github.com/ggml-org/llama.cpp/issues/9291)
-
-## Hot topics
-
- **Hugging Face cache migration: models downloaded with `-hf` are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.**
- **[guide : using the new WebUI of llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/16938)**
- [guide : running gpt-oss with llama.cpp](https://github.com/ggml-org/llama.cpp/discussions/15396)
- [[FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗](https://github.com/ggml-org/llama.cpp/discussions/15313)
- Support for the `gpt-oss` model with native MXFP4 format has been added | [PR](https://github.com/ggml-org/llama.cpp/pull/15091) | [Collaboration with NVIDIA](https://blogs.nvidia.com/blog/rtx-ai-garage-openai-oss) | [Comment](https://github.com/ggml-org/llama.cpp/discussions/15095)
- Multimodal support arrived in `llama-server`: [#12898](https://github.com/ggml-org/llama.cpp/pull/12898) | [documentation](./docs/multimodal.md)
- VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
- Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
- Hugging Face Inference Endpoints now support GGUF out of the box! https://github.com/ggml-org/llama.cpp/discussions/9669
- Hugging Face GGUF editor: [discussion](https://github.com/ggml-org/llama.cpp/discussions/9268) | [tool](https://huggingface.co/spaces/CISCai/gguf-editor)
- WebGPU support is now available in the browser, see a blog/demo introducing it [here](https://reeselevine.github.io/llamas-on-the-web/).
-
----
+</div>

 ## Quick start

-Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:
+A few options to get `llama.cpp` installed on your machine:

- Install `llama.cpp` using [brew, nix, winget, or conda-forge](docs/install.md)
+- Visit https://llama.app and follow the instructions
 - Run with Docker - see our [Docker documentation](docs/docker.md)
 - Download pre-built binaries from the [releases page](https://github.com/ggml-org/llama.cpp/releases)
 - Build from source by cloning this repository - check out [our build guide](docs/build.md)

-Once installed, you'll need a model to work with. Head to the [Obtaining and quantizing models](#obtaining-and-quantizing-models) section to learn more.
-
-Example command:
+Once installed:

 ```sh
-# Use a local model file
-llama-cli -m my_model.gguf
-
-# Or download and run a model directly from Hugging Face
-llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
+# Download and run a model directly from Hugging Face
+llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF

 # Launch OpenAI-compatible API server
-llama-server -hf ggml-org/gemma-3-1b-it-GGUF
+llama serve -hf ggml-org/Qwen3.5-0.8B-GGUF
 ```

+<table align="center">
+    <tr>
+        <td align="center" width=50%>
+            <img width="1310" height="888" alt="VLM session with `llama cli`" src="https://github.com/user-attachments/assets/88726b48-1713-48aa-a525-95a02e78afc4" />
+            <i>VLM session with <b>llama cli</b></i>
+        </td>
+        <td align="center">
+            <img width="1392" height="958" alt="Built-in web UI against `llama serve` running Qwen 3.6" src="https://github.com/user-attachments/assets/b402f972-2e32-4def-8771-8d849f08cf2e" />
+            <i>Built-in web UI against <b>llama serve</b></i>
+        </td>
+    </tr>
+<table>
+
 ## Description

-The main goal of `llama.cpp` is to enable LLM inference with minimal setup and state-of-the-art performance on a wide
-range of hardware - locally and in the cloud.
+The main goal of `llama.cpp` is to enable LLM (and VLM) inference with minimal setup and state-of-the-art performance on
+a wide range of hardware - locally and in the cloud.

 - Plain C/C++ implementation without any dependencies
 - Apple silicon is a first-class citizen - optimized via ARM NEON, Accelerate and Metal frameworks
@@ -71,467 +62,40 @@ range of hardware - locally and in the cloud.
 - Vulkan and SYCL backend support
 - CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity

-The `llama.cpp` project is the main playground for developing new features for the [ggml](https://github.com/ggml-org/ggml) library.
-
-<details>
-<summary>Models</summary>
-
-Typically finetunes of the base models below are supported as well.
-
-Instructions for adding support for new models: [HOWTO-add-model.md](docs/development/HOWTO-add-model.md)
-
-#### Text-only
-
- [X] LLaMA 🦙
- [x] LLaMA 2 🦙🦙
- [x] LLaMA 3 🦙🦙🦙
- [X] [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1)
- [x] [Mixtral MoE](https://huggingface.co/models?search=mistral-ai/Mixtral)
- [x] [DBRX](https://huggingface.co/databricks/dbrx-instruct)
- [x] [Jamba](https://huggingface.co/ai21labs)
- [X] [Falcon](https://huggingface.co/models?search=tiiuae/falcon)
- [X] [Chinese LLaMA / Alpaca](https://github.com/ymcui/Chinese-LLaMA-Alpaca) and [Chinese LLaMA-2 / Alpaca-2](https://github.com/ymcui/Chinese-LLaMA-Alpaca-2)
- [X] [Vigogne (French)](https://github.com/bofenghuang/vigogne)
- [X] [BERT](https://github.com/ggml-org/llama.cpp/pull/5423)
- [X] [Koala](https://bair.berkeley.edu/blog/2023/04/03/koala/)
- [X] [Baichuan 1 & 2](https://huggingface.co/models?search=baichuan-inc/Baichuan) + [derivations](https://huggingface.co/hiyouga/baichuan-7b-sft)
- [X] [Aquila 1 & 2](https://huggingface.co/models?search=BAAI/Aquila)
- [X] [Starcoder models](https://github.com/ggml-org/llama.cpp/pull/3187)
- [X] [Refact](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
- [X] [MPT](https://github.com/ggml-org/llama.cpp/pull/3417)
- [X] [Bloom](https://github.com/ggml-org/llama.cpp/pull/3553)
- [x] [Yi models](https://huggingface.co/models?search=01-ai/Yi)
- [X] [StableLM models](https://huggingface.co/stabilityai)
- [x] [Deepseek models](https://huggingface.co/models?search=deepseek-ai/deepseek)
- [x] [Qwen models](https://huggingface.co/models?search=Qwen/Qwen)
- [x] [PLaMo-13B](https://github.com/ggml-org/llama.cpp/pull/3557)
- [x] [Phi models](https://huggingface.co/models?search=microsoft/phi)
- [x] [PhiMoE](https://github.com/ggml-org/llama.cpp/pull/11003)
- [x] [GPT-2](https://huggingface.co/gpt2)
- [x] [Orion 14B](https://github.com/ggml-org/llama.cpp/pull/5118)
- [x] [InternLM2](https://huggingface.co/models?search=internlm2)
- [x] [CodeShell](https://github.com/WisdomShell/codeshell)
- [x] [Gemma](https://ai.google.dev/gemma)
- [x] [Mamba](https://github.com/state-spaces/mamba)
- [x] [Grok-1](https://huggingface.co/keyfan/grok-1-hf)
- [x] [Xverse](https://huggingface.co/models?search=xverse)
- [x] [Command-R models](https://huggingface.co/models?search=CohereForAI/c4ai-command-r)
- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
- [x] [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) + [GritLM-8x7B](https://huggingface.co/GritLM/GritLM-8x7B)
- [x] [OLMo](https://allenai.org/olmo)
- [x] [OLMo 2](https://allenai.org/olmo)
- [x] [OLMoE](https://huggingface.co/allenai/OLMoE-1B-7B-0924)
- [x] [Granite models](https://huggingface.co/collections/ibm-granite/granite-code-models-6624c5cec322e4c148c8b330)
- [x] [GPT-NeoX](https://github.com/EleutherAI/gpt-neox) + [Pythia](https://github.com/EleutherAI/pythia)
- [x] [Snowflake-Arctic MoE](https://huggingface.co/collections/Snowflake/arctic-66290090abe542894a5ac520)
- [x] [Smaug](https://huggingface.co/models?search=Smaug)
- [x] [Poro 34B](https://huggingface.co/LumiOpen/Poro-34B)
- [x] [Bitnet b1.58 models](https://huggingface.co/1bitLLM)
- [x] [Flan T5](https://huggingface.co/models?search=flan-t5)
- [x] [Open Elm models](https://huggingface.co/collections/apple/openelm-instruct-models-6619ad295d7ae9f868b759ca)
- [x] [ChatGLM3-6b](https://huggingface.co/THUDM/chatglm3-6b) + [ChatGLM4-9b](https://huggingface.co/THUDM/glm-4-9b) + [GLMEdge-1.5b](https://huggingface.co/THUDM/glm-edge-1.5b-chat) + [GLMEdge-4b](https://huggingface.co/THUDM/glm-edge-4b-chat)
- [x] [GLM-4-0414](https://huggingface.co/collections/THUDM/glm-4-0414-67f3cbcb34dd9d252707cb2e)
- [x] [SmolLM](https://huggingface.co/collections/HuggingFaceTB/smollm-6695016cad7167254ce15966)
- [x] [EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct)
- [x] [FalconMamba Models](https://huggingface.co/collections/tiiuae/falconmamba-7b-66b9a580324dd1598b0f6d4a)
- [x] [Jais](https://huggingface.co/inceptionai/jais-13b-chat)
- [x] [Bielik-11B-v2.3](https://huggingface.co/collections/speakleash/bielik-11b-v23-66ee813238d9b526a072408a)
- [x] [RWKV-7](https://huggingface.co/collections/shoumenchougou/rwkv7-gxx-gguf)
- [x] [RWKV-6](https://github.com/BlinkDL/RWKV-LM)
- [x] [QRWKV-6](https://huggingface.co/recursal/QRWKV6-32B-Instruct-Preview-v0.1)
- [x] [GigaChat-20B-A3B](https://huggingface.co/ai-sage/GigaChat-20B-A3B-instruct)
- [X] [Trillion-7B-preview](https://huggingface.co/trillionlabs/Trillion-7B-preview)
- [x] [Ling models](https://huggingface.co/collections/inclusionAI/ling-67c51c85b34a7ea0aba94c32)
- [x] [Liquid LFM2 models](https://huggingface.co/collections/LiquidAI/lfm2)
- [x] [Liquid LFM2.5 models](https://huggingface.co/collections/LiquidAI/lfm25)
- [x] [Liquid Nanos](https://huggingface.co/collections/LiquidAI/liquid-nanos)
- [x] [Hunyuan models](https://huggingface.co/collections/tencent/hunyuan-dense-model-6890632cda26b19119c9c5e7)
- [x] [BailingMoeV2 (Ring/Ling 2.0) models](https://huggingface.co/collections/inclusionAI/ling-v2-68bf1dd2fc34c306c1fa6f86)
- [x] [Mellum models](https://huggingface.co/JetBrains/models?search=mellum)
-
-#### Multimodal
-
- [x] [LLaVA 1.5 models](https://huggingface.co/collections/liuhaotian/llava-15-653aac15d994e992e2677a7e), [LLaVA 1.6 models](https://huggingface.co/collections/liuhaotian/llava-16-65b9e40155f60fd046a5ccf2)
- [x] [BakLLaVA](https://huggingface.co/models?search=SkunkworksAI/Bakllava)
- [x] [Obsidian](https://huggingface.co/NousResearch/Obsidian-3B-V0.5)
- [x] [ShareGPT4V](https://huggingface.co/models?search=Lin-Chen/ShareGPT4V)
- [x] [MobileVLM 1.7B/3B models](https://huggingface.co/models?search=mobileVLM)
- [x] [Yi-VL](https://huggingface.co/models?search=Yi-VL)
- [x] [Mini CPM](https://huggingface.co/models?search=MiniCPM)
- [x] [Moondream](https://huggingface.co/vikhyatk/moondream2)
- [x] [Bunny](https://github.com/BAAI-DCAI/Bunny)
- [x] [GLM-EDGE](https://huggingface.co/models?search=glm-edge)
- [x] [Qwen2-VL](https://huggingface.co/collections/Qwen/qwen2-vl-66cee7455501d7126940800d)
- [x] [LFM2-VL](https://huggingface.co/collections/LiquidAI/lfm2-vl-68963bbc84a610f7638d5ffa)
-
-</details>
-
-<details>
-<summary>Bindings</summary>
-
- Python: [ddh0/easy-llama](https://github.com/ddh0/easy-llama)
- Python: [abetlen/llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
- Go: [go-skynet/go-llama.cpp](https://github.com/go-skynet/go-llama.cpp)
- Node.js: [withcatai/node-llama-cpp](https://github.com/withcatai/node-llama-cpp)
- JS/TS (llama.cpp server client): [lgrammel/modelfusion](https://modelfusion.dev/integration/model-provider/llamacpp)
- JS/TS (Programmable Prompt Engine CLI): [offline-ai/cli](https://github.com/offline-ai/cli)
- JavaScript/Wasm (works in browser): [tangledgroup/llama-cpp-wasm](https://github.com/tangledgroup/llama-cpp-wasm)
- Typescript/Wasm (nicer API, available on npm): [ngxson/wllama](https://github.com/ngxson/wllama)
- Ruby: [yoshoku/llama_cpp.rb](https://github.com/yoshoku/llama_cpp.rb)
- Ruby: [docusealco/rllama](https://github.com/docusealco/rllama)
- Rust (more features): [edgenai/llama_cpp-rs](https://github.com/edgenai/llama_cpp-rs)
- Rust (nicer API): [mdrokz/rust-llama.cpp](https://github.com/mdrokz/rust-llama.cpp)
- Rust (more direct bindings): [utilityai/llama-cpp-rs](https://github.com/utilityai/llama-cpp-rs)
- Rust (automated build from crates.io): [ShelbyJenkins/llm_client](https://github.com/ShelbyJenkins/llm_client)
- C#/.NET: [SciSharp/LLamaSharp](https://github.com/SciSharp/LLamaSharp)
- C#/VB.NET (more features - community license): [LM-Kit.NET](https://docs.lm-kit.com/lm-kit-net/index.html)
- Scala 3: [donderom/llm4s](https://github.com/donderom/llm4s)
- Clojure: [phronmophobic/llama.clj](https://github.com/phronmophobic/llama.clj)
- React Native: [mybigday/llama.rn](https://github.com/mybigday/llama.rn)
- Java: [kherud/java-llama.cpp](https://github.com/kherud/java-llama.cpp)
- Java: [QuasarByte/llama-cpp-jna](https://github.com/QuasarByte/llama-cpp-jna)
- Zig: [deins/llama.cpp.zig](https://github.com/Deins/llama.cpp.zig)
- Flutter/Dart: [netdur/llama_cpp_dart](https://github.com/netdur/llama_cpp_dart)
- Flutter: [xuegao-tzx/Fllama](https://github.com/xuegao-tzx/Fllama)
- PHP (API bindings and features built on top of llama.cpp): [distantmagic/resonance](https://github.com/distantmagic/resonance) [(more info)](https://github.com/ggml-org/llama.cpp/pull/6326)
- Guile Scheme: [guile_llama_cpp](https://savannah.nongnu.org/projects/guile-llama-cpp)
- Swift [srgtuszy/llama-cpp-swift](https://github.com/srgtuszy/llama-cpp-swift)
- Swift [ShenghaiWang/SwiftLlama](https://github.com/ShenghaiWang/SwiftLlama)
- Delphi [Embarcadero/llama-cpp-delphi](https://github.com/Embarcadero/llama-cpp-delphi)
- Go (no CGo needed): [hybridgroup/yzma](https://github.com/hybridgroup/yzma)
- Android: [llama.android](/examples/llama.android)
-
-</details>
-
-<details>
-<summary>UIs</summary>
-
-*(to have a project listed here, it should clearly state that it depends on `llama.cpp`)*
-
- [AI Sublime Text plugin](https://github.com/yaroslavyaroslav/OpenAI-sublime-text) (MIT)
- [BonzAI App](https://apps.apple.com/us/app/bonzai-your-local-ai-agent/id6752847988) (proprietary)
- [cztomsik/ava](https://github.com/cztomsik/ava) (MIT)
- [Dot](https://github.com/alexpinel/Dot) (GPL)
- [eva](https://github.com/ylsdamxssjxxdd/eva) (MIT)
- [iohub/collama](https://github.com/iohub/coLLaMA) (Apache-2.0)
- [janhq/jan](https://github.com/janhq/jan) (AGPL)
- [johnbean393/Sidekick](https://github.com/johnbean393/Sidekick) (MIT)
- [KanTV](https://github.com/zhouwg/kantv?tab=readme-ov-file) (Apache-2.0)
- [KodiBot](https://github.com/firatkiral/kodibot) (GPL)
- [llama.vim](https://github.com/ggml-org/llama.vim) (MIT)
- [LARS](https://github.com/abgulati/LARS) (AGPL)
- [Llama Assistant](https://github.com/vietanhdev/llama-assistant) (GPL)
- [LlamaLib](https://github.com/undreamai/LlamaLib) (Apache-2.0)
- [LLMFarm](https://github.com/guinmoon/LLMFarm?tab=readme-ov-file) (MIT)
- [LLMUnity](https://github.com/undreamai/LLMUnity) (MIT)
- [LMStudio](https://lmstudio.ai/) (proprietary)
- [LocalAI](https://github.com/mudler/LocalAI) (MIT)
- [LostRuins/koboldcpp](https://github.com/LostRuins/koboldcpp) (AGPL)
- [MindMac](https://mindmac.app) (proprietary)
- [MindWorkAI/AI-Studio](https://github.com/MindWorkAI/AI-Studio) (FSL-1.1-MIT)
- [Mobile-Artificial-Intelligence/maid](https://github.com/Mobile-Artificial-Intelligence/maid) (MIT)
- [Mozilla-Ocho/llamafile](https://github.com/Mozilla-Ocho/llamafile) (Apache-2.0)
- [nat/openplayground](https://github.com/nat/openplayground) (MIT)
- [nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) (MIT)
- [ollama/ollama](https://github.com/ollama/ollama) (MIT)
- [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) (AGPL)
- [PocketPal AI](https://github.com/a-ghorbani/pocketpal-ai) (MIT)
- [psugihara/FreeChat](https://github.com/psugihara/FreeChat) (MIT)
- [ptsochantaris/emeltal](https://github.com/ptsochantaris/emeltal) (MIT)
- [pythops/tenere](https://github.com/pythops/tenere) (AGPL)
- [ramalama](https://github.com/containers/ramalama) (MIT)
- [semperai/amica](https://github.com/semperai/amica) (MIT)
- [withcatai/catai](https://github.com/withcatai/catai) (MIT)
- [Autopen](https://github.com/blackhole89/autopen) (GPL)
-
-</details>
-
-<details>
-<summary>Tools</summary>
-
- [akx/ggify](https://github.com/akx/ggify) – download PyTorch models from Hugging Face Hub and convert them to GGML
- [akx/ollama-dl](https://github.com/akx/ollama-dl) – download models from the Ollama library to be used directly with llama.cpp
- [crashr/gppm](https://github.com/crashr/gppm) – launch llama.cpp instances utilizing NVIDIA Tesla P40 or P100 GPUs with reduced idle power consumption
- [gpustack/gguf-parser](https://github.com/gpustack/gguf-parser-go/tree/main/cmd/gguf-parser) - review/check the GGUF file and estimate the memory usage
- [Styled Lines](https://marketplace.unity.com/packages/tools/generative-ai/styled-lines-llama-cpp-model-292902) (proprietary licensed, async wrapper of inference part for game development in Unity3d with pre-built Mobile and Web platform wrappers and a model example)
- [unslothai/unsloth](https://github.com/unslothai/unsloth) – 🦥 exports/saves fine-tuned and trained models to GGUF (Apache-2.0)
-
-</details>
-
-<details>
-<summary>Infrastructure</summary>
-
- [Paddler](https://github.com/intentee/paddler) - Open-source LLMOps platform for hosting and scaling AI in your own infrastructure
- [GPUStack](https://github.com/gpustack/gpustack) - Manage GPU clusters for running LLMs
- [llama_cpp_canister](https://github.com/onicai/llama_cpp_canister) - llama.cpp as a smart contract on the Internet Computer, using WebAssembly
- [llama-swap](https://github.com/mostlygeek/llama-swap) - transparent proxy that adds automatic model switching with llama-server
- [Kalavai](https://github.com/kalavai-net/kalavai-client) - Crowdsource end to end LLM deployment at any scale
- [llmaz](https://github.com/InftyAI/llmaz) - ☸️ Easy, advanced inference platform for large language models on Kubernetes.
- [LLMKube](https://github.com/defilantech/llmkube) - Kubernetes operator for llama.cpp with multi-GPU and Apple Silicon Metal
-  support"
-</details>
-
-<details>
-<summary>Games</summary>
-
- [Lucy's Labyrinth](https://github.com/MorganRO8/Lucys_Labyrinth) - A simple maze game where agents controlled by an AI model will try to trick you.
-
-</details>
-
+The `llama.cpp` project is build on top of the [ggml](https://github.com/ggml-org/ggml) library.

 ## Supported backends

 | Backend | Target devices |
 | --- | --- |
-| [Metal](docs/build.md#metal-build) | Apple Silicon |
 | [BLAS](docs/build.md#blas-build) | All |
 | [BLIS](docs/backend/BLIS.md) | All |
-| [SYCL](docs/backend/SYCL.md) | Intel GPU |
-| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
-| [MUSA](docs/build.md#musa) | Moore Threads GPU |
+| [CANN](docs/build.md#cann) | Ascend NPU |
 | [CUDA](docs/build.md#cuda) | Nvidia GPU |
 | [HIP](docs/build.md#hip) | AMD GPU |
-| [ZenDNN](docs/build.md#zendnn) | AMD CPU |
-| [Vulkan](docs/build.md#vulkan) | GPU |
-| [CANN](docs/build.md#cann) | Ascend NPU |
-| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
-| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
-| [WebGPU](docs/build.md#webgpu) | All |
-| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
 | [Hexagon [In Progress]](docs/backend/snapdragon/README.md) | Snapdragon |
+| [IBM zDNN](docs/backend/zDNN.md) | IBM Z & LinuxONE |
+| [MUSA](docs/build.md#musa) | Moore Threads GPU |
+| [Metal](docs/build.md#metal-build) | Apple Silicon |
+| [OpenCL](docs/backend/OPENCL.md) | Adreno GPU |
+| [OpenVINO [In Progress]](docs/backend/OPENVINO.md) | Intel CPUs, GPUs, and NPUs |
+| [RPC](https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc) | All |
+| [SYCL](docs/backend/SYCL.md) | Intel GPU |
 | [VirtGPU](docs/backend/VirtGPU.md) | VirtGPU APIR |
+| [Vulkan](docs/build.md#vulkan) | GPU |
+| [WebGPU](docs/build.md#webgpu) | All |
+| [ZenDNN](docs/build.md#zendnn) | AMD CPU |

-## Obtaining and quantizing models
+## Documentation

-The [Hugging Face](https://huggingface.co) platform hosts a [number of LLMs](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
-
- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
- [LLaMA](https://huggingface.co/models?sort=trending&search=llama+gguf)
-
-You can either manually download the GGUF file or directly use any `llama.cpp`-compatible models from [Hugging Face](https://huggingface.co/) or other model hosting sites, by using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
-
-```sh
-llama-cli -hf ggml-org/gemma-3-1b-it-GGUF
-```
-
-By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable `MODEL_ENDPOINT`. The `MODEL_ENDPOINT` must point to a Hugging Face compatible API endpoint.
-
-After downloading a model, use the CLI tools to run it locally - see below.
-
-`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
-
-The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
-
- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)
-
-To learn more about model quantization, [read this documentation](tools/quantize/README.md)
-
-## [`llama-cli`](tools/cli)
-
-#### A CLI tool for accessing and experimenting with most of `llama.cpp`'s functionality.
-
- <details open>
-    <summary>Run in conversation mode</summary>
-
-    Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding `-cnv` and specifying a suitable chat template with `--chat-template NAME`
-
-    ```bash
-    llama-cli -m model.gguf
-
-    # > hi, who are you?
-    # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
-    #
-    # > what is 1+1?
-    # Easy peasy! The answer to 1+1 is... 2!
-    ```
-
-    </details>
-
- <details>
-    <summary>Run in conversation mode with custom chat template</summary>
-
-    ```bash
-    # use the "chatml" template (use -h to see the list of supported templates)
-    llama-cli -m model.gguf -cnv --chat-template chatml
-
-    # use a custom template
-    llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'
-    ```
-
-    </details>
-
- <details>
-    <summary>Constrain the output with a custom grammar</summary>
-
-    ```bash
-    llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
-
-    # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}
-    ```
-
-    The [grammars/](grammars/) folder contains a handful of sample grammars. To write your own, check out the [GBNF Guide](grammars/README.md).
-
-    For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/
-
-    </details>
-
-
-## [`llama-server`](tools/server)
-
-#### A lightweight, [OpenAI API](https://github.com/openai/openai-openapi) compatible, HTTP server for serving LLMs.
-
- <details open>
-    <summary>Start a local HTTP server with default configuration on port 8080</summary>
-
-    ```bash
-    llama-server -m model.gguf --port 8080
-
-    # Basic web UI can be accessed via browser: http://localhost:8080
-    # Chat completion endpoint: http://localhost:8080/v1/chat/completions
-    ```
-
-    </details>
-
- <details>
-    <summary>Support multiple-users and parallel decoding</summary>
-
-    ```bash
-    # up to 4 concurrent requests, each with 4096 max context
-    llama-server -m model.gguf -c 16384 -np 4
-    ```
-
-    </details>
-
- <details>
-    <summary>Enable speculative decoding</summary>
-
-    ```bash
-    # the draft.gguf model should be a small variant of the target model.gguf
-    llama-server -m model.gguf -md draft.gguf
-    ```
-
-    </details>
-
- <details>
-    <summary>Serve an embedding model</summary>
-
-    ```bash
-    # use the /embedding endpoint
-    llama-server -m model.gguf --embedding --pooling cls -ub 8192
-    ```
-
-    </details>
-
- <details>
-    <summary>Serve a reranking model</summary>
-
-    ```bash
-    # use the /reranking endpoint
-    llama-server -m model.gguf --reranking
-    ```
-
-    </details>
-
- <details>
-    <summary>Constrain all outputs with a grammar</summary>
-
-    ```bash
-    # custom grammar
-    llama-server -m model.gguf --grammar-file grammar.gbnf
-
-    # JSON
-    llama-server -m model.gguf --grammar-file grammars/json.gbnf
-    ```
-
-    </details>
-
-
-## [`llama-perplexity`](tools/perplexity)
-
-#### A tool for measuring the [perplexity](tools/perplexity/README.md) [^1] (and other quality metrics) of a model over a given text.
-
- <details open>
-    <summary>Measure the perplexity over a text file</summary>
-
-    ```bash
-    llama-perplexity -m model.gguf -f file.txt
-
-    # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
-    # Final estimate: PPL = 5.4007 +/- 0.67339
-    ```
-
-    </details>
-
- <details>
-    <summary>Measure KL divergence</summary>
-
-    ```bash
-    # TODO
-    ```
-
-    </details>
-
-[^1]: [https://huggingface.co/docs/transformers/perplexity](https://huggingface.co/docs/transformers/perplexity)
-
-## [`llama-bench`](tools/llama-bench)
-
-#### Benchmark the performance of the inference for various parameters.
-
- <details open>
-    <summary>Run default benchmark</summary>
-
-    ```bash
-    llama-bench -m model.gguf
-
-    # Output:
-    # | model               |       size |     params | backend    | threads |          test |                  t/s |
-    # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
-    # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
-    # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
-    #
-    # build: 3e0ba0e60 (4229)
-    ```
-
-    </details>
-
-## [`llama-simple`](examples/simple)
-
-#### A minimal example for implementing apps with `llama.cpp`. Useful for developers.
-
- <details>
-    <summary>Basic text completion</summary>
-
-    ```bash
-    llama-simple -m model.gguf
-
-    # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of
-    ```
-
-    </details>
-
-
-## Contributing
-
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
- Any help with managing issues, PRs and projects is very appreciated!
- See [good first issues](https://github.com/ggml-org/llama.cpp/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) for tasks suitable for first contributions
- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information
- Make sure to read this: [Inference at the edge](https://github.com/ggml-org/llama.cpp/discussions/205)
- A bit of backstory for those who are interested: [Changelog podcast](https://changelog.com/podcast/532)
-
-## Other documentation
+#### Tools

 - [cli](tools/cli/README.md)
 - [completion](tools/completion/README.md)
 - [server](tools/server/README.md)
 - [GBNF grammars](grammars/README.md)

-#### Development documentation
+#### Development

 - [How to build](docs/build.md)
 - [Running on Docker](docs/docker.md)
@@ -539,63 +103,19 @@ To learn more about model quantization, [read this documentation](tools/quantize
 - [Multi-GPU usage](docs/multi-gpu.md)
 - [Performance troubleshooting](docs/development/token_generation_performance_tips.md)
 - [GGML tips & tricks](https://github.com/ggml-org/llama.cpp/wiki/GGML-Tips-&-Tricks)
+- [XCFramework](docs/xcframework.md)
+- [Completions](docs/completions.md)
+- [Models](docs/models.md)

-#### Seminal papers and background on the models
+## Contributing

-If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:
- LLaMA:
-    - [Introducing LLaMA: A foundational, 65-billion-parameter large language model](https://ai.facebook.com/blog/large-language-model-llama-meta-ai/)
-    - [LLaMA: Open and Efficient Foundation Language Models](https://arxiv.org/abs/2302.13971)
- GPT-3
-    - [Language Models are Few-Shot Learners](https://arxiv.org/abs/2005.14165)
- GPT-3.5 / InstructGPT / ChatGPT:
-    - [Aligning language models to follow instructions](https://openai.com/research/instruction-following)
-    - [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155)
+- Contributors can open PRs
+- Collaborators will be invited based on contributions
+- Maintainers can push to branches in the `llama.cpp` repo and merge PRs into the `master` branch
+- Any help with managing issues, PRs and projects is very appreciated!
+- Read the [CONTRIBUTING.md](CONTRIBUTING.md) for more information

-## XCFramework
-The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS,
-and macOS. It can be used in Swift projects without the need to compile the
-library from source. For example:
-```swift
-// swift-tools-version: 5.10
-// The swift-tools-version declares the minimum version of Swift required to build this package.
-
-import PackageDescription
-
-let package = Package(
-    name: "MyLlamaPackage",
-    targets: [
-        .executableTarget(
-            name: "MyLlamaPackage",
-            dependencies: [
-                "LlamaFramework"
-            ]),
-        .binaryTarget(
-            name: "LlamaFramework",
-            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
-            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
-        )
-    ]
-)
-```
-The above example is using an intermediate build `b5046` of the library. This can be modified
-to use a different version by changing the URL and checksum.
-
-## Completions
-Command-line completion is available for some environments.
-
-#### Bash Completion
-```bash
-$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
-$ source ~/.llama-completion.bash
-```
-Optionally this can be added to your `.bashrc` or `.bash_profile` to load it
-automatically. For example:
-```console
-$ echo "source ~/.llama-completion.bash" >> ~/.bashrc
-```
-
-## Dependencies
+## Acknowledgements

 - [yhirose/cpp-httplib](https://github.com/yhirose/cpp-httplib) - Single-header HTTP server, used by `llama-server` - MIT license
 - [stb-image](https://github.com/nothings/stb) - Single-header image format decoder, used by multimodal subsystem - Public domain
@@ -1476,6 +1476,20 @@ std::string common_get_model_endpoint() {
    return model_endpoint;
 }

+char * common_get_model_or_exit(int argc, char * argv[]) {
+    if (argc > 1) {
+        return argv[1];
+    }
+
+    char * path = getenv("LLAMACPP_TEST_MODELFILE");
+    if (!path || strlen(path) == 0) {
+        fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE=<gguf_model_path> to silence this warning and run this test.\n\033[0m");
+        exit(EXIT_SUCCESS);
+    }
+
+    return path;
+}
+
 common_context_seq_rm_type common_context_can_seq_rm(llama_context * ctx) {
    auto * mem = llama_get_memory(ctx);
    if (mem == nullptr) {
@@ -930,6 +930,9 @@ void common_set_adapter_lora(struct llama_context * ctx, std::vector<common_adap
 // model endpoint from env
 std::string common_get_model_endpoint();

+// for testing purposes
+char * common_get_model_or_exit(int, char*[]);
+
 //
 // Context utils
 //
@@ -788,7 +788,7 @@ use 1 SYCL GPUs: [0] with Max compute units:512
 | Name              | Value            | Function                                                                                                                  |
 |-------------------|------------------|---------------------------------------------------------------------------------------------------------------------------|
 | GGML_SYCL_DEBUG   | 0 (default) or 1 | Enable log function by macro: GGML_SYCL_DEBUG                                                                             |
-| GGML_SYCL_DEV2DEV_MEMCPY | 0 (default) or 1 | Choose the SYCL or L0 API in dev2dev memory copy.<br>Value: <br>*  0: SYCL API (default)<br>* 1: L0 API -- L0 API is found to lead to abnormal crash in some case. This debug flag is used to check the issue.|
+| GGML_SYCL_DEV2DEV_MEMCPY | 0 (default), 1, 2 | Choose the method of dev2dev memory copy.<br>Value: <br>*  0: SYCL API (default), only support dGPUs.<br>* 1: L0 API -- Better performance, only support dGPUs, found to lead to abnormal crash in some case. <br>* 2: Host Forward -- Most stable method for all cases (including iGPU + dGPU*N), but with lower performance (-2% to -5%).<br>SYCL & L0 API are easy to be impacted by Intel GPU driver issue. When you meet the garbled output or crash issues in multiple GPUs case, try with this debug flag to work around or check the issue.|
 | GGML_SYCL_ENABLE_FLASH_ATTN | 1 (default) or 0| Enable Flash-Attention. It can reduce memory usage. The performance impact depends on the LLM.|
 | GGML_SYCL_ENABLE_OPT | 0 or 1 (default)| Enable optimize features for Intel GPUs. (Recommended to 0 for Intel devices older than Gen 10) |
 | GGML_SYCL_ENABLE_GRAPH | 0 (default) or 1 | Enable running computations through SYCL Graphs feature. Disabled by default because SYCL Graph is still on development, no better performance. |
@@ -0,0 +1,17 @@
+# Completions
+
+Command-line completion is available for some environments.
+
+## Bash Completion
+
+```bash
+$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
+$ source ~/.llama-completion.bash
+```
+
+Optionally this can be added to your `.bashrc` or `.bash_profile` to load it
+automatically. For example:
+
+```console
+$ echo "source ~/.llama-completion.bash" >> ~/.bashrc
+```
@@ -0,0 +1,26 @@
+# Obtaining and quantizing models
+
+The [Hugging Face](https://huggingface.co) platform hosts [thousands of models](https://huggingface.co/models?library=gguf&sort=trending) compatible with `llama.cpp`:
+
+- [Trending](https://huggingface.co/models?library=gguf&sort=trending)
+
+You can use any `llama.cpp`-compatible model from [Hugging Face](https://huggingface.co/) using this CLI argument: `-hf <user>/<model>[:quant]`. For example:
+
+```sh
+llama cli -hf ggml-org/gemma-3-1b-it-GGUF
+```
+
+You can use the same CLI invocation to download from other sites, by pointing the `MODEL_ENDPOINT` environment variable to an endpoint compatible with the Hugging Face API.
+`llama.cpp` can also run models you have downloaded locally to your filesystem.
+
+After downloading a model, use the CLI tools to run it locally - see below.
+
+`llama.cpp` requires the model to be stored in the [GGUF](https://github.com/ggml-org/ggml/blob/master/docs/gguf.md) file format. Models in other data formats can be converted to GGUF using the `convert_*.py` Python scripts in this repo.
+To learn more about model quantization, [read this documentation](../tools/quantize/README.md)
+
+The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with `llama.cpp`:
+
+- Use the [GGUF-my-repo space](https://huggingface.co/spaces/ggml-org/gguf-my-repo) to convert to GGUF format and quantize model weights to smaller sizes
+- Use the [GGUF-my-LoRA space](https://huggingface.co/spaces/ggml-org/gguf-my-lora) to convert LoRA adapters to GGUF format (more info: https://github.com/ggml-org/llama.cpp/discussions/10123)
+- Use the [GGUF-editor space](https://huggingface.co/spaces/CISCai/gguf-editor) to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
+- Use the [Inference Endpoints](https://ui.endpoints.huggingface.co/) to directly host `llama.cpp` in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)
@@ -0,0 +1,31 @@
+# XCFramework
+
+The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS,
+and macOS. It can be used in Swift projects without the need to compile the
+library from source. For example:
+
+```swift
+// swift-tools-version: 5.10
+// The swift-tools-version declares the minimum version of Swift required to build this package.
+
+import PackageDescription
+
+let package = Package(
+    name: "MyLlamaPackage",
+    targets: [
+        .executableTarget(
+            name: "MyLlamaPackage",
+            dependencies: [
+                "LlamaFramework"
+            ]),
+        .binaryTarget(
+            name: "LlamaFramework",
+            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
+            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
+        )
+    ]
+)
+```
+
+The above example is using an intermediate build `b5046` of the library. This can be modified
+to use a different version by changing the URL and checksum.
@@ -4,7 +4,7 @@ project("ggml" C CXX ASM)

 ### GGML Version
 set(GGML_VERSION_MAJOR 0)
-set(GGML_VERSION_MINOR 17)
+set(GGML_VERSION_MINOR 18)
 set(GGML_VERSION_PATCH 0)
 set(GGML_VERSION_BASE "${GGML_VERSION_MAJOR}.${GGML_VERSION_MINOR}.${GGML_VERSION_PATCH}")

@@ -469,6 +469,8 @@ static bool ggml_backend_cpu_device_supports_op(ggml_backend_dev_t dev, const st
            return (src0->type == GGML_TYPE_F32 ||
                    ((src0->type == GGML_TYPE_F16 || ggml_is_quantized(src0->type)) && src0->ne[2] == src1->ne[2] && src0->ne[3] == src1->ne[3])) &&
                src1->type == GGML_TYPE_F32 && op->type == GGML_TYPE_F32;
+        case GGML_OP_CONV_2D:
+            return ggml_is_contiguous(op->src[0]);
        default:
            return true;
    }
@@ -977,6 +977,13 @@ struct ggml_cuda_type_traits<GGML_TYPE_Q1_0> {
    static constexpr int qi = QI1_0;
 };

+template<>
+struct ggml_cuda_type_traits<GGML_TYPE_Q2_0> {
+    static constexpr int qk = QK2_0;
+    static constexpr int qr = QR2_0;
+    static constexpr int qi = QI2_0;
+};
+
 template<>
 struct ggml_cuda_type_traits<GGML_TYPE_Q4_0> {
    static constexpr int qk = QK4_0;
@@ -126,6 +126,7 @@ void ggml_cuda_op_conv2d(ggml_backend_cuda_context & ctx, ggml_tensor * dst) {
    const float *       X_D    = (const float *) input->data;
    float *             Y_D    = (float *) dst->data;

+    GGML_ASSERT(ggml_is_contiguous(input));
    GGML_ASSERT(ggml_is_contiguous(kernel));
    GGML_ASSERT(kernel->type == GGML_TYPE_F16 || kernel->type == GGML_TYPE_F32);

@@ -459,6 +459,8 @@ to_bf16_cuda_t ggml_get_to_bf16_cuda(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:
            return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_cont_cuda<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            return dequantize_row_q4_0_cuda;
        case GGML_TYPE_Q4_1:
@@ -514,6 +516,8 @@ to_fp16_cuda_t ggml_get_to_fp16_cuda(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:
            return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_cont_cuda<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            return dequantize_row_q4_0_cuda;
        case GGML_TYPE_Q4_1:
@@ -572,6 +576,8 @@ to_fp32_cuda_t ggml_get_to_fp32_cuda(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:
            return dequantize_block_cont_cuda<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_cont_cuda<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            return dequantize_row_q4_0_cuda;
        case GGML_TYPE_Q4_1:
@@ -629,6 +635,8 @@ to_fp16_nc_cuda_t ggml_get_to_fp16_nc_cuda(ggml_type type) {
            return convert_unary_cuda<float>;
        case GGML_TYPE_Q1_0:
            return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_cuda<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
        case GGML_TYPE_Q4_1:
@@ -652,6 +660,8 @@ to_bf16_nc_cuda_t ggml_get_to_bf16_nc_cuda(ggml_type type) {
            return convert_unary_cuda<float, nv_bfloat16>;
        case GGML_TYPE_Q1_0:
            return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_cuda<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
        case GGML_TYPE_Q4_1:
@@ -675,6 +685,8 @@ to_fp32_nc_cuda_t ggml_get_to_fp32_nc_cuda(ggml_type type) {
            return convert_unary_cuda<half, float>;
        case GGML_TYPE_Q1_0:
            return dequantize_block_cuda<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_cuda<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            return dequantize_block_cuda<QK4_0, QR4_0, dequantize_q4_0>;
        case GGML_TYPE_Q4_1:
@@ -23,6 +23,26 @@ static __device__ __forceinline__ void dequantize_q1_0(const void * vx, const in
    v.y = (2*bit_1 - 1) * d;
 }

+static __device__ __forceinline__ void dequantize_q2_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
+    const block_q2_0 * x = (const block_q2_0 *) vx;
+
+    const float d = x[ib].d;
+
+    // Q2_0: 2 bits per element, 4 elements per byte.
+    // Stored code c in {0,1,2,3} maps to symbol s = c - 1 in {-1, 0, +1, +2}.
+    const int byte_index_0 = iqs / 4;
+    const int bit_offset_0 = (iqs % 4) * 2;
+
+    const int byte_index_1 = (iqs + 1) / 4;
+    const int bit_offset_1 = ((iqs + 1) % 4) * 2;
+
+    const int c0 = (x[ib].qs[byte_index_0] >> bit_offset_0) & 0x3;
+    const int c1 = (x[ib].qs[byte_index_1] >> bit_offset_1) & 0x3;
+
+    v.x = (c0 - 1) * d;
+    v.y = (c1 - 1) * d;
+}
+
 static __device__ __forceinline__ void dequantize_q4_0(const void * vx, const int64_t ib, const int iqs, float2 & v){
    const block_q4_0 * x = (const block_q4_0 *) vx;

@@ -320,6 +320,10 @@ static void ggml_cuda_get_rows_switch_src0_type(
            get_rows_cuda_q<QK1_0, QR1_0, dequantize_q1_0>(src0_d, src1_d, dst_d,
                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
            break;
+        case GGML_TYPE_Q2_0:
+            get_rows_cuda_q<QK2_0, QR2_0, dequantize_q2_0>(src0_d, src1_d, dst_d,
+                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
+            break;
        case GGML_TYPE_Q4_0:
            get_rows_cuda_q<QK4_0, QR4_0, dequantize_q4_0>(src0_d, src1_d, dst_d,
                ne00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb1, nb2, nb3, stream);
@@ -1836,6 +1836,20 @@ static void ggml_cuda_mul_mat(ggml_backend_cuda_context & ctx, const ggml_tensor
        ggml_cuda_mul_mat_vec_f(ctx, src0, src1, nullptr, dst);
        return;
    }
+    // A transposed vector can still use MMVQ (i.e. ne01 == 1)
+    if (ne01 == 1 && ne11 > MMVF_MAX_BATCH_SIZE && ne2 == 1 && ne3 == 1
+            && src0->type == GGML_TYPE_F32
+            && ggml_is_contiguous(src0) && ggml_is_contiguous(src1) && ggml_is_contiguous(dst)
+            && ggml_cuda_should_use_mmvf(src1->type, cc, src1->ne, src1->nb, /*ne11 =*/ 1)) {
+        ggml_tensor dst_vec = *dst;
+        dst_vec.ne[0] = ne11;
+        dst_vec.ne[1] = 1;
+        dst_vec.nb[1] = dst_vec.nb[0]*ne11;
+        dst_vec.nb[2] = dst_vec.nb[1];
+        dst_vec.nb[3] = dst_vec.nb[1];
+        ggml_cuda_mul_mat_vec_f(ctx, src1, src0, nullptr, &dst_vec);
+        return;
+    }
    if (ggml_cuda_should_use_mmf(src0->type, cc, warp_size, src0->ne, src0->nb, ne11, /*mul_mat_id =*/ false)) {
        ggml_cuda_mul_mat_f(ctx, src0, src1, nullptr, dst);
        return;
@@ -4802,6 +4816,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                    case GGML_TYPE_F32:
                    case GGML_TYPE_F16:
                    case GGML_TYPE_Q1_0:
+                    case GGML_TYPE_Q2_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
@@ -4840,6 +4855,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
                    case GGML_TYPE_BF16:
                    case GGML_TYPE_I32:
                    case GGML_TYPE_Q1_0:
+                    case GGML_TYPE_Q2_0:
                    case GGML_TYPE_Q4_0:
                    case GGML_TYPE_Q4_1:
                    case GGML_TYPE_Q5_0:
@@ -5089,7 +5105,7 @@ static bool ggml_backend_cuda_device_supports_op(ggml_backend_dev_t dev, const g
        case GGML_OP_IM2COL:
        case GGML_OP_IM2COL_3D:
        case GGML_OP_CONV_2D:
-            return true;
+            return (ggml_is_contiguous(op->src[0]) && ggml_is_contiguous(op->src[1]));
        case GGML_OP_CONV_2D_DW:
            return op->src[0]->type == GGML_TYPE_F32;
        case GGML_OP_CONV_TRANSPOSE_2D:
@@ -16,6 +16,23 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 256, 1, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
    CASE(GGML_TYPE_Q1_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);

+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 256, 1, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+
    CASE(GGML_TYPE_Q4_0, 256, 1, 128,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
    CASE(GGML_TYPE_Q4_0, 256, 1, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
    CASE(GGML_TYPE_Q4_0, 256, 1, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
@@ -7,6 +7,14 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 512, 1, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
    CASE(GGML_TYPE_Q1_0, 512, 1, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);

+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+    CASE(GGML_TYPE_Q2_0, 512, 1, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, false);
+
    CASE(GGML_TYPE_Q4_0, 512, 1, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
    CASE(GGML_TYPE_Q4_0, 512, 1, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
    CASE(GGML_TYPE_Q4_0, 512, 1, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, true, true);
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 256, 2, 64,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 64,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);

+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 64,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+
    CASE(GGML_TYPE_Q4_0, 256, 2, 64,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 256, 2, 64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 256, 2, 64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 256, 2, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);

+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  24, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  40, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+
    CASE(GGML_TYPE_Q4_0, 256, 2, 128,   8, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 256, 2, 128,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 256, 2, 128,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 256, 2, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);

    CASE(GGML_TYPE_Q4_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 256, 2, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);

    CASE(GGML_TYPE_Q4_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
@@ -11,6 +11,18 @@ static constexpr __host__ __device__ ggml_cuda_mmq_config ggml_cuda_mmq_get_conf
    CASE(GGML_TYPE_Q1_0, 256, 2, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
    CASE(GGML_TYPE_Q1_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  48, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 128, 2,  64,  64, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  80, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128,  96, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 112, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);
+    CASE(GGML_TYPE_Q2_0, 256, 2, 128, 128, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, false);

    CASE(GGML_TYPE_Q4_0, 128, 2,  64,  16, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
    CASE(GGML_TYPE_Q4_0, 128, 2,  64,  32, GGML_CUDA_MMQ_SRAM_LAYOUT_Q8_0, MMQ_ITER_K, false, true);
@@ -95,6 +95,103 @@ template <ggml_type type, int J, bool fallback> static __device__ __forceinline_
    }
 }

+template <ggml_type type, int J, bool fallback> static __device__ __forceinline__ void ggml_cuda_mmq_load_tiles_q2_0(
+        const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
+    constexpr int warp_size   = ggml_cuda_get_physical_warp_size();
+    constexpr int nwarps      = ggml_cuda_mmq_get_nthreads(type, J, fallback) / warp_size;
+    constexpr int I           = ggml_cuda_mmq_get_I(type, J, fallback);
+    constexpr int sram_stride = ggml_cuda_mmq_get_sram_stride(type, J, fallback);
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + 2*MMQ_TILE_NE_K);
+#else
+    constexpr tile_x_sizes txs = mmq_get_dp4a_tile_x_sizes(GGML_TYPE_Q8_0, I);
+    int   * x_qs = (int   *)  x_tile;
+    float * x_df = (float *) (x_qs + txs.qs);
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+
+    constexpr int blocks_per_iter = MMQ_ITER_K / QK2_0;
+    constexpr int threads_per_row = blocks_per_iter * QI2_0;
+    constexpr int nrows = warp_size / threads_per_row;
+    constexpr int scale_entries_per_block = QK2_0 / QK8_1;
+    constexpr int scale_entries_per_row = blocks_per_iter * scale_entries_per_block;
+
+    const int txi  = threadIdx.x % threads_per_row;
+    const int kbx  = txi / QI2_0;
+    const int kqsx = txi % QI2_0;
+
+#pragma unroll
+    for (int i0 = 0; i0 < I; i0 += nrows*nwarps) {
+        int i = i0 + threadIdx.y*nrows + threadIdx.x/threads_per_row;
+
+        if (fallback) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_0 * bxi = (const block_q2_0 *) x + kbx0 + i*stride + kbx;
+        // Each 32-element chunk occupies 8 bytes of qs (32 elements * 2 bits = 64 bits)
+        const int qs_offset = 8*kqsx;
+        const int qs0 = bxi->qs[qs_offset + 0] | (bxi->qs[qs_offset + 1] << 8) |
+                        (bxi->qs[qs_offset + 2] << 16) | (bxi->qs[qs_offset + 3] << 24);
+        const int qs1 = bxi->qs[qs_offset + 4] | (bxi->qs[qs_offset + 5] << 8) |
+                        (bxi->qs[qs_offset + 6] << 16) | (bxi->qs[qs_offset + 7] << 24);
+
+        // Unpack 32 2-bit codes into 8 int32s, each holding 4 signed int8s in {-1,0,1,2}.
+        int unpacked_bytes[8];
+#pragma unroll
+        for (int j = 0; j < 4; ++j) {
+            const int shift = j * 8;
+            const int codes = (qs0 >> shift) & 0xFF;
+            const int c0 = ((codes >> 0) & 0x3) - 1;
+            const int c1 = ((codes >> 2) & 0x3) - 1;
+            const int c2 = ((codes >> 4) & 0x3) - 1;
+            const int c3 = ((codes >> 6) & 0x3) - 1;
+            unpacked_bytes[j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
+        }
+#pragma unroll
+        for (int j = 0; j < 4; ++j) {
+            const int shift = j * 8;
+            const int codes = (qs1 >> shift) & 0xFF;
+            const int c0 = ((codes >> 0) & 0x3) - 1;
+            const int c1 = ((codes >> 2) & 0x3) - 1;
+            const int c2 = ((codes >> 4) & 0x3) - 1;
+            const int c3 = ((codes >> 6) & 0x3) - 1;
+            unpacked_bytes[4 + j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
+        }
+
+        const int dst_offset = kbx*(scale_entries_per_block*QI8_0) + kqsx*QI8_0;
+#pragma unroll
+        for (int j = 0; j < 8; ++j) {
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+            x_qs[i*sram_stride           + dst_offset + j] = unpacked_bytes[j];
+#else
+            x_qs[i*(2*MMQ_TILE_NE_K + 1) + dst_offset + j] = unpacked_bytes[j];
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+        }
+    }
+
+    const int ksx = threadIdx.x % scale_entries_per_row;
+    const int scale_block = ksx / scale_entries_per_block;
+
+#pragma unroll
+    for (int i0 = 0; i0 < I; i0 += nwarps) {
+        int i = i0 + threadIdx.y;
+
+        if (fallback) {
+            i = min(i, i_max);
+        }
+
+        const block_q2_0 * bxi = (const block_q2_0 *) x + kbx0 + i*stride + scale_block;
+
+#if defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+        x_df[i*sram_stride                           + ksx] = bxi->d;
+#else
+        x_df[i*(2*MMQ_TILE_NE_K/QI8_0) + i/(QI8_0/2) + ksx] = bxi->d;
+#endif // defined(AMD_MFMA_AVAILABLE) || defined(TURING_MMA_AVAILABLE) || defined(AMD_WMMA_AVAILABLE)
+    }
+}
+
 template <ggml_type type, int J, bool fallback> static __device__ __forceinline__ void ggml_cuda_mmq_load_tiles_q4_0(
        const char * __restrict__ x, int * __restrict__ x_tile, const int kbx0, const int i_max, const int stride) {
    constexpr int warp_size   = ggml_cuda_get_physical_warp_size();
@@ -10,6 +10,9 @@ static void ggml_cuda_mul_mat_q_switch_type(ggml_backend_cuda_context & ctx, con
        case GGML_TYPE_Q1_0:
            mul_mat_q_case<GGML_TYPE_Q1_0>(ctx, args, stream);
            break;
+        case GGML_TYPE_Q2_0:
+            mul_mat_q_case<GGML_TYPE_Q2_0>(ctx, args, stream);
+            break;
        case GGML_TYPE_Q4_0:
            mul_mat_q_case<GGML_TYPE_Q4_0>(ctx, args, stream);
            break;
@@ -262,6 +265,7 @@ bool ggml_cuda_should_use_mmq(enum ggml_type type, int cc, int64_t ne11, int64_t

    switch (type) {
        case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
@@ -60,6 +60,7 @@ static_assert(sizeof(block_fp4_mmq)  == sizeof(block_q8_1_mmq),    "Unexpected b
 static mmq_q8_1_ds_layout mmq_get_q8_1_ds_layout(const ggml_type type_x) {
    switch (type_x) {
        case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
            return MMQ_Q8_1_DS_LAYOUT_D4;
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
@@ -385,6 +386,7 @@ static constexpr __device__ int ggml_cuda_mmq_get_rows_per_warp(ggml_type type,
 static constexpr __host__ __device__ tile_x_sizes mmq_get_dp4a_tile_x_sizes(ggml_type type, int I) {
    switch (type) {
        case GGML_TYPE_Q1_0:    return MMQ_DP4A_TXS_Q8_0;
+        case GGML_TYPE_Q2_0:    return MMQ_DP4A_TXS_Q8_0;
        case GGML_TYPE_Q4_0:    return MMQ_DP4A_TXS_Q4_0;
        case GGML_TYPE_Q4_1:    return MMQ_DP4A_TXS_Q4_1;
        case GGML_TYPE_Q5_0:    return MMQ_DP4A_TXS_Q8_0;
@@ -542,6 +544,12 @@ static constexpr __device__ ggml_cuda_mmq_util_funcs ggml_cuda_mmq_get_util_func
                    ggml_cuda_mmq_load_tiles_q1_0<type, J, fallback>,
                    ggml_cuda_mmq_vec_dot_q8_0_q8_1_dp4a<type, J, fallback>,
                    ggml_cuda_mmq_write_back_dp4a<type, J, fallback>);
+            case GGML_TYPE_Q2_0:
+                return ggml_cuda_mmq_util_funcs(
+                    VDR_Q2_0_Q8_1_MMQ,
+                    ggml_cuda_mmq_load_tiles_q2_0<type, J, fallback>,
+                    ggml_cuda_mmq_vec_dot_q8_0_q8_1_dp4a<type, J, fallback>,
+                    ggml_cuda_mmq_write_back_dp4a<type, J, fallback>);
            case GGML_TYPE_Q4_0:
                return ggml_cuda_mmq_util_funcs(
                    VDR_Q4_0_Q8_1_MMQ,
@@ -700,6 +708,12 @@ static constexpr __device__ ggml_cuda_mmq_util_funcs ggml_cuda_mmq_get_util_func
                ggml_cuda_mmq_load_tiles_q1_0<type, J, fallback>,
                ggml_cuda_mmq_vec_dot_q8_0_q8_1_mma<type, J, fallback, MMQ_Q8_1_DS_LAYOUT_D4>,
                ggml_cuda_mmq_write_back_mma<type, J, fallback>);
+        case GGML_TYPE_Q2_0:
+            return ggml_cuda_mmq_util_funcs(
+                -1,
+                ggml_cuda_mmq_load_tiles_q2_0<type, J, fallback>,
+                ggml_cuda_mmq_vec_dot_q8_0_q8_1_mma<type, J, fallback, MMQ_Q8_1_DS_LAYOUT_D4>,
+                ggml_cuda_mmq_write_back_mma<type, J, fallback>);
        case GGML_TYPE_Q4_0:
            return ggml_cuda_mmq_util_funcs(
                -1,
@@ -1550,6 +1564,7 @@ void mul_mat_q_case(ggml_backend_cuda_context & ctx, const mmq_args & args, cuda
    template void mul_mat_q_case<type>(ggml_backend_cuda_context & ctx, const mmq_args & args, cudaStream_t stream) \

 extern DECL_MMQ_CASE(GGML_TYPE_Q1_0);
+extern DECL_MMQ_CASE(GGML_TYPE_Q2_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_0);
 extern DECL_MMQ_CASE(GGML_TYPE_Q4_1);
 extern DECL_MMQ_CASE(GGML_TYPE_Q5_0);
@@ -10,6 +10,7 @@ typedef float (*vec_dot_q_cuda_t)(const void * __restrict__ vbq, const block_q8_
 static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:    return vec_dot_q1_0_q8_1;
+        case GGML_TYPE_Q2_0:    return vec_dot_q2_0_q8_1;
        case GGML_TYPE_Q4_0:    return vec_dot_q4_0_q8_1;
        case GGML_TYPE_Q4_1:    return vec_dot_q4_1_q8_1;
        case GGML_TYPE_Q5_0:    return vec_dot_q5_0_q8_1;
@@ -38,6 +39,7 @@ static constexpr __device__ vec_dot_q_cuda_t get_vec_dot_q_cuda(ggml_type type)
 static constexpr __host__ __device__ int get_vdr_mmvq(ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:    return VDR_Q1_0_Q8_1_MMVQ;
+        case GGML_TYPE_Q2_0:    return VDR_Q2_0_Q8_1_MMVQ;
        case GGML_TYPE_Q4_0:    return VDR_Q4_0_Q8_1_MMVQ;
        case GGML_TYPE_Q4_1:    return VDR_Q4_1_Q8_1_MMVQ;
        case GGML_TYPE_Q5_0:    return VDR_Q5_0_Q8_1_MMVQ;
@@ -1010,6 +1012,12 @@ static void mul_mat_vec_q_switch_type(
                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
            break;
+        case GGML_TYPE_Q2_0:
+            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q2_0>
+                (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
+                 nchannels_x, nchannels_y, nchannels_dst, stride_channel_x, stride_channel_y, stride_channel_dst,
+                 nsamples_x, nsamples_dst, stride_sample_x, stride_sample_y, stride_sample_dst, ids_stride, stream);
+            break;
        case GGML_TYPE_Q4_0:
            mul_mat_vec_q_switch_ncols_dst<GGML_TYPE_Q4_0>
                (vx, vy, ids, fusion, dst, ncols_x, nrows_x, ncols_dst, stride_row_x, stride_col_y, stride_col_dst,
@@ -36,6 +36,7 @@ SOURCE_FATTN_MMA_CASE = "DECL_FATTN_MMA_F16_CASE({head_size_kq}, {head_size_v},

 TYPES_MMQ = [
    "GGML_TYPE_Q1_0",
+    "GGML_TYPE_Q2_0",
    "GGML_TYPE_Q4_0", "GGML_TYPE_Q4_1", "GGML_TYPE_Q5_0", "GGML_TYPE_Q5_1", "GGML_TYPE_Q8_0",
    "GGML_TYPE_Q2_K", "GGML_TYPE_Q3_K", "GGML_TYPE_Q4_K", "GGML_TYPE_Q5_K", "GGML_TYPE_Q6_K",
    "GGML_TYPE_IQ2_XXS", "GGML_TYPE_IQ2_XS", "GGML_TYPE_IQ2_S", "GGML_TYPE_IQ3_XXS", "GGML_TYPE_IQ3_S",
@@ -0,0 +1,5 @@
+// This file has been autogenerated by generate_cu_files.py, do not edit manually.
+
+#include "../mmq.cuh"
+
+DECL_MMQ_CASE(GGML_TYPE_Q2_0);
@@ -109,6 +109,9 @@ static __device__ __forceinline__ uint32_t unpack_ksigns(const uint8_t v) {
 #define VDR_Q1_0_Q8_1_MMVQ 1  // Process one 32-element chunk at a time for parallelism
 #define VDR_Q1_0_Q8_1_MMQ  4  // Q1_0 has 128 bits (4 ints) per block

+#define VDR_Q2_0_Q8_1_MMVQ 1  // Process one 32-element chunk at a time for parallelism
+#define VDR_Q2_0_Q8_1_MMQ  2  // Q2_0 group 64: 128 bits (4 ints) per block, 2 32-element chunks
+
 #define VDR_Q4_0_Q8_1_MMVQ 2
 #define VDR_Q4_0_Q8_1_MMQ  4

@@ -722,6 +725,64 @@ static __device__ __forceinline__ float vec_dot_q1_0_q8_1(
    return d1 * d8 * sumi;
 }

+static __device__ __forceinline__ float vec_dot_q2_0_q8_1(
+    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {
+
+    const block_q2_0 * bq2_0 = (const block_q2_0 *) vbq + kbx;
+
+    // Q2_0 (group 64): 64 elements with ONE scale, 2 bits per element (4 elements per byte)
+    // Q8_1: 32 elements per block with individual scales
+    // iqs selects which of the 2 chunks of 32 elements to process (0-1)
+
+    const float d2 = bq2_0->d;
+
+    // Process only the chunk specified by iqs
+    const block_q8_1 * bq8_1_chunk = bq8_1 + iqs;
+
+    // Load 64 bits (8 bytes) for this chunk from Q2_0: bytes [8*iqs, 8*iqs+8)
+    const int offset = iqs * 8;
+    const int v0 = bq2_0->qs[offset + 0] | (bq2_0->qs[offset + 1] << 8) |
+                   (bq2_0->qs[offset + 2] << 16) | (bq2_0->qs[offset + 3] << 24);
+    const int v1 = bq2_0->qs[offset + 4] | (bq2_0->qs[offset + 5] << 8) |
+                   (bq2_0->qs[offset + 6] << 16) | (bq2_0->qs[offset + 7] << 24);
+
+    // Unpack 32 2-bit codes into 8 int32s, each holding 4 signed int8 symbols in {-1,0,1,2}.
+    // Stored code c in {0,1,2,3} -> symbol s = c - 1.
+    int vi_bytes[8];
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        const int shift = j * 8;
+        const int codes = (v0 >> shift) & 0xFF;
+        const int c0 = ((codes >> 0) & 0x3) - 1;
+        const int c1 = ((codes >> 2) & 0x3) - 1;
+        const int c2 = ((codes >> 4) & 0x3) - 1;
+        const int c3 = ((codes >> 6) & 0x3) - 1;
+        vi_bytes[j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
+    }
+#pragma unroll
+    for (int j = 0; j < 4; ++j) {
+        const int shift = j * 8;
+        const int codes = (v1 >> shift) & 0xFF;
+        const int c0 = ((codes >> 0) & 0x3) - 1;
+        const int c1 = ((codes >> 2) & 0x3) - 1;
+        const int c2 = ((codes >> 4) & 0x3) - 1;
+        const int c3 = ((codes >> 6) & 0x3) - 1;
+        vi_bytes[4 + j] = (c0 & 0xFF) | ((c1 & 0xFF) << 8) | ((c2 & 0xFF) << 16) | ((c3 & 0xFF) << 24);
+    }
+
+    // Compute dot product for this 32-element chunk
+    int sumi = 0;
+#pragma unroll
+    for (int j = 0; j < 8; ++j) {
+        const int u = get_int_b4(bq8_1_chunk->qs, j);
+        sumi = ggml_cuda_dp4a(vi_bytes[j], u, sumi);
+    }
+
+    // Apply Q2_0's single scale and this chunk's Q8_1 scale
+    const float d8 = __low2float(bq8_1_chunk->ds);
+    return d2 * d8 * sumi;
+}
+
 static __device__ __forceinline__ float vec_dot_q4_0_q8_1(
    const void * __restrict__ vbq, const block_q8_1 * __restrict__ bq8_1, const int & kbx, const int & iqs) {

@@ -26,6 +26,7 @@
 #include "dmmv.hpp"
 #include "element_wise.hpp"
 #include "fattn.hpp"
+#include "fusion.hpp"
 #include "gated_delta_net.hpp"
 #include "gla.hpp"
 #include "im2col.hpp"
@@ -133,6 +133,7 @@ enum ggml_sycl_backend_gpu_mode {
 enum ggml_sycl_dev2dev_memcpy_mode {
  DEV2DEV_MEMCPY_SYCL = 0,
  DEV2DEV_MEMCPY_L0 = 1,
+  DEV2DEV_MEMCPY_FORWARD = 2
 };

 static_assert(sizeof(sycl::half) == sizeof(ggml_fp16_t), "wrong fp16 size");
@@ -644,6 +644,8 @@ to_fp16_sycl_t ggml_get_to_fp16_sycl(ggml_type type, ggml_tensor * dst) {
    switch (type) {
        case GGML_TYPE_Q1_0:
            return dequantize_block_sycl<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_sycl<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            if (dst->src[0]->extra &&
                ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
@@ -728,6 +730,8 @@ to_fp32_sycl_t ggml_get_to_fp32_sycl(ggml_type type, ggml_tensor *dst) {
    switch (type) {
        case GGML_TYPE_Q1_0:
            return dequantize_block_sycl<QK1_0, QR1_0, dequantize_q1_0>;
+        case GGML_TYPE_Q2_0:
+            return dequantize_block_sycl<QK2_0, QR2_0, dequantize_q2_0>;
        case GGML_TYPE_Q4_0:
            if (dst->src[0]->extra &&
                ((ggml_tensor_extra_gpu*)dst->src[0]->extra)->optimized_feature.reorder) {
@@ -8,7 +8,6 @@
 #include "ggml-sycl/presets.hpp"
 #include "ggml.h"

-
 static void cpy_1_f32_f32(const char * cxi, char * cdsti) {
    const float * xi   = (const float *) cxi;
    float *       dsti = (float *) cdsti;
@@ -151,6 +150,20 @@ static void cpy_blck_q8_0_f32(const char * cxi, char * cdsti) {
    }
 }

+static void cpy_blck_q2_0_f32(const char * cxi, char * cdsti) {
+    const block_q2_0 * xi = (const block_q2_0 *) cxi;
+    float * cdstf = (float *) cdsti;
+
+    const float d = xi->d;
+
+    for (int j = 0; j < QK2_0; ++j) {
+        const int byte_index = j / 4;
+        const int bit_offset = (j % 4) * 2;
+        const int q = (xi->qs[byte_index] >> bit_offset) & 0x3;
+        cdstf[j] = (float) (q - 1) * d;
+    }
+}
+


 template <dequantize_kernel_t dequant, int qk> static void cpy_blck_q_f32(const char * cxi, char * cdsti) {
@@ -256,7 +269,7 @@ static void ggml_cpy_f16_f32_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_f16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -274,7 +287,7 @@ static void ggml_cpy_f32_f32_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_f32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -292,7 +305,7 @@ static void ggml_cpy_f32_f16_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_f32_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -308,7 +321,7 @@ static void ggml_cpy_f32_i32_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_f32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -324,7 +337,7 @@ static void ggml_cpy_i32_f32_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_i32_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -338,7 +351,7 @@ static void ggml_cpy_f32_q8_0_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK8_0 == 0);
    const int num_blocks = ne / QK8_0;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f32_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
                         });
@@ -350,12 +363,25 @@ static void ggml_cpy_q8_0_f32_sycl(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ne;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_q_f32<cpy_blck_q8_0_f32, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
                         });
 }

+static void ggml_cpy_q2_0_f32_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ne;
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+            cpy_q_f32<cpy_blck_q2_0_f32, QK2_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
+                                                ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
 static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
@@ -363,7 +389,7 @@ static void ggml_cpy_f32_q4_0_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK4_0 == 0);
    const int num_blocks = ne / QK4_0;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f32_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
                         });
@@ -375,7 +401,8 @@ static void ggml_cpy_q4_0_f32_sycl(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ne;
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_f32<cpy_blck_q_f32<dequantize_q4_0, QK4_0>, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                     nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                     item_ct1);
@@ -389,7 +416,7 @@ static void ggml_cpy_f32_q4_1_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK4_1 == 0);
    const int num_blocks = ne / QK4_1;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f32_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
                         });
@@ -401,7 +428,8 @@ static void ggml_cpy_q4_1_f32_sycl(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ne;
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_f32<cpy_blck_q_f32<dequantize_q4_1, QK4_1>, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                     nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                     item_ct1);
@@ -415,7 +443,7 @@ static void ggml_cpy_f32_q5_0_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK5_0 == 0);
    const int num_blocks = ne / QK5_0;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
                             cpy_f32_q<cpy_blck_f32_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
                         });
@@ -427,7 +455,8 @@ static void ggml_cpy_q5_0_f32_sycl(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ne;
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_f32<cpy_blck_q_f32<dequantize_q5_0, QK5_0>, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                     nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                     item_ct1);
@@ -441,7 +470,7 @@ static void ggml_cpy_f32_q5_1_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK5_1 == 0);
    const int num_blocks = ne / QK5_1;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f32_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
                         });
@@ -453,7 +482,8 @@ static void ggml_cpy_q5_1_f32_sycl(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ne;
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_f32<cpy_blck_q_f32<dequantize_q5_1, QK5_1>, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02,
                                                                     nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13,
                                                                     item_ct1);
@@ -466,7 +496,8 @@ static void ggml_cpy_mxfp4_f32_sycl(const char * cx, char * cdst, const int ne,
                                    const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ne;
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
            cpy_q_f32<cpy_blck_q_f32<dequantize_mxfp4, QK_MXFP4>, QK_MXFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00,
                                                                             nb01, nb02, nb03, ne10, ne11, ne12,
                                                                             nb10, nb11, nb12, nb13, item_ct1);
@@ -480,7 +511,8 @@ static void ggml_cpy_f32_iq4_nl_sycl(const char * cx, char * cdst, const int ne,
    GGML_ASSERT(ne % QK4_NL == 0);
    const int num_blocks = ne / QK4_NL;
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
            cpy_f32_q<cpy_blck_f32_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11,
                                                   ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
@@ -526,7 +558,7 @@ static void ggml_cpy_f16_q4_0_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK4_0 == 0);
    const int num_blocks = ne / QK4_0;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f16_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02,
                                                                 nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@@ -540,7 +572,7 @@ static void ggml_cpy_f16_q4_1_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK4_1 == 0);
    const int num_blocks = ne / QK4_1;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f16_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02,
                                                                 nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@@ -554,7 +586,7 @@ static void ggml_cpy_f16_q5_0_sycl(const char * cx, char * cdst, const int ne, c
    GGML_ASSERT(ne % QK5_0 == 0);
    const int num_blocks = ne / QK5_0;
    stream->parallel_for(sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks), sycl::range<3>(1, 1, 1)),
-                         [=](sycl::nd_item<3> item_ct1) {
+                         [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                             cpy_f32_q<cpy_blck_f16_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02,
                                                                 nb00, nb01, nb02, nb03,
                                                                 ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
@@ -564,6 +596,7 @@ static void ggml_cpy_f16_q5_0_sycl(const char * cx, char * cdst, const int ne, c
 static bool ggml_sycl_is_quantized_type(enum ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
@@ -594,6 +627,7 @@ static bool ggml_sycl_is_quantized_type(enum ggml_type type) {
 static bool ggml_sycl_can_quantize_rows_sycl(enum ggml_type type) {
    switch (type) {
        case GGML_TYPE_Q1_0:
+        case GGML_TYPE_Q2_0:
        case GGML_TYPE_Q4_0:
        case GGML_TYPE_Q4_1:
        case GGML_TYPE_Q5_0:
@@ -651,7 +685,8 @@ static void ggml_sycl_quantize_rows_q(const char * cx, char * cdst, const int64_
    constexpr int block_size = 256;
    const int64_t grid_size = ceil_div(total_blocks, (int64_t) block_size);

-    stream->parallel_for(sycl::nd_range<1>(grid_size * block_size, block_size), [=](sycl::nd_item<1> item_ct1) {
+    stream->parallel_for(sycl::nd_range<1>(grid_size * block_size, block_size),
+                        [=](sycl::nd_item<1> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
        const int64_t block_idx = item_ct1.get_global_linear_id();
        if (block_idx >= total_blocks) {
            return;
@@ -708,6 +743,11 @@ static void ggml_sycl_quantize_rows_sycl(const char * cx, char * cdst, const ggm
                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
                                                                            nb12, nb13, stream);
            break;
+        case GGML_TYPE_Q2_0:
+            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q2_0, QK2_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
+                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
+                                                                            nb12, nb13, stream);
+            break;
        case GGML_TYPE_Q5_1:
            ggml_sycl_quantize_rows_q<SrcScalar, cpy_blck_f32_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01,
                                                                            nb02, nb03, ne10, ne11, ne12, nb10, nb11,
@@ -760,7 +800,7 @@ static void ggml_cpy_f16_f16_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_f16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -779,7 +819,7 @@ static void ggml_cpy_i16_i16_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_i16_i16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -798,7 +838,7 @@ static void ggml_cpy_i32_i32_sycl(const char * cx, char * cdst, const int ne, co
        stream->parallel_for(
            sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-            [=](sycl::nd_item<3> item_ct1) {
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
                cpy_f32_f16<cpy_1_i32_i32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
            });
@@ -812,7 +852,8 @@ static void ggml_cpy_q8_0_q8_0(const char * cx, char * cdst, const int ne, const
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q8_0, QK8_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -825,7 +866,8 @@ static void ggml_cpy_q5_0_q5_0(const char * cx, char * cdst, const int ne, const
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q5_0, QK5_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -839,7 +881,8 @@ static void ggml_cpy_q5_1_q5_1(const char * cx, char * cdst, const int ne, const

    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
-                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+                              sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+                              [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q5_1, QK5_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -851,7 +894,8 @@ static void ggml_cpy_q4_0_q4_0(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q4_0, QK4_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -864,7 +908,8 @@ static void ggml_cpy_q4_1_q4_1(const char * cx, char * cdst, const int ne, const

   const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
   stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q4_1, QK4_1>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -875,18 +920,32 @@ static void ggml_cpy_q1_0_q1_0(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
            cpy_q_q<block_q1_0, QK1_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }

+static void ggml_cpy_q2_0_q2_0(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
+                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
+                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
+                                   const int nb12, const int nb13, queue_ptr stream) {
+    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
+    stream->parallel_for(
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
+            cpy_q_q<block_q2_0, QK2_0>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
+        });
+}
+
 static void ggml_cpy_mxfp4_mxfp4(const char * cx, char * cdst, const int ne, const int ne00, const int ne01,
                                   const int ne02, const int nb00, const int nb01, const int nb02, const int nb03,
                                   const int ne10, const int ne11, const int ne12, const int nb10, const int nb11,
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
            cpy_q_q<block_mxfp4, QK_MXFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -897,7 +956,8 @@ static void ggml_cpy_nvfp4_nvfp4(const char * cx, char * cdst, const int ne, con
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_nvfp4, QK_NVFP4>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -908,7 +968,8 @@ static void ggml_cpy_q2_K_q2_K(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q2_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -919,7 +980,8 @@ static void ggml_cpy_q3_K_q3_K(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q3_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -930,7 +992,8 @@ static void ggml_cpy_q4_K_q4_K(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q4_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -941,7 +1004,8 @@ static void ggml_cpy_q5_K_q5_K(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q5_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -952,7 +1016,8 @@ static void ggml_cpy_q6_K_q6_K(const char * cx, char * cdst, const int ne, const
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_q6_K, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -963,7 +1028,8 @@ static void ggml_cpy_iq2_xxs_iq2_xxs(const char * cx, char * cdst, const int ne,
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq2_xxs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -974,7 +1040,8 @@ static void ggml_cpy_iq2_xs_iq2_xs(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq2_xs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -985,7 +1052,8 @@ static void ggml_cpy_iq2_s_iq2_s(const char * cx, char * cdst, const int ne, con
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq2_s, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -996,7 +1064,8 @@ static void ggml_cpy_iq3_xxs_iq3_xxs(const char * cx, char * cdst, const int ne,
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq3_xxs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -1007,7 +1076,8 @@ static void ggml_cpy_iq1_s_iq1_s(const char * cx, char * cdst, const int ne, con
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq1_s, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -1018,7 +1088,8 @@ static void ggml_cpy_iq1_m_iq1_m(const char * cx, char * cdst, const int ne, con
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq1_m, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -1029,7 +1100,8 @@ static void ggml_cpy_iq4_nl_iq4_nl(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq4_nl, QK4_NL>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -1040,7 +1112,8 @@ static void ggml_cpy_iq3_s_iq3_s(const char * cx, char * cdst, const int ne, con
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq3_s, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -1051,7 +1124,8 @@ static void ggml_cpy_iq4_xs_iq4_xs(const char * cx, char * cdst, const int ne, c
                                   const int nb12, const int nb13, queue_ptr stream) {
    const int num_blocks = ceil_div(ne, SYCL_CPY_BLOCK_SIZE);
    stream->parallel_for(
-        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)), [=](sycl::nd_item<3> item_ct1) {
+        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE), sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_q_q<block_iq4_xs, QK_K>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, item_ct1);
        });
 }
@@ -1065,7 +1139,7 @@ static void ggml_cpy_f32_bf16_sycl(const char * cx, char * cdst, const int ne, c
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_f32_f16<cpy_1_f32_bf16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
        });
@@ -1079,7 +1153,7 @@ static void ggml_cpy_bf16_f32_sycl(const char * cx, char * cdst, const int ne, c
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_f32_f16<cpy_1_bf16_f32>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
        });
@@ -1093,7 +1167,7 @@ static void ggml_cpy_bf16_bf16_sycl(const char * cx, char * cdst, const int ne,
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_f32_f16<cpy_1_bf16_bf16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                            nb10, nb11, nb12, nb13, item_ct1);
        });
@@ -1107,7 +1181,7 @@ static void ggml_cpy_f16_bf16_sycl(const char * cx, char * cdst, const int ne, c
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_f32_f16<cpy_1_f16_bf16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
        });
@@ -1121,7 +1195,7 @@ static void ggml_cpy_bf16_f16_sycl(const char * cx, char * cdst, const int ne, c
    stream->parallel_for(
        sycl::nd_range<3>(sycl::range<3>(1, 1, num_blocks) * sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE),
                          sycl::range<3>(1, 1, SYCL_CPY_BLOCK_SIZE)),
-        [=](sycl::nd_item<3> item_ct1) {
+        [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]]{
            cpy_f32_f16<cpy_1_bf16_f16>(cx, cdst, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
                                           nb10, nb11, nb12, nb13, item_ct1);
        });
@@ -1213,6 +1287,9 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
    } else if (src0->type == GGML_TYPE_Q8_0 && src1->type == GGML_TYPE_F32) {
        ggml_cpy_q8_0_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q2_0 && src1->type == GGML_TYPE_F32) {
+        ggml_cpy_q2_0_f32_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12,
+                               nb10, nb11, nb12, nb13, main_stream);
    } else if (src0->type == GGML_TYPE_F32 && src1->type == GGML_TYPE_Q5_0) {
        ggml_cpy_f32_q5_0_sycl(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10,
                               nb11, nb12, nb13, main_stream);
@@ -1243,6 +1320,8 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
        ggml_cpy_q4_1_q4_1(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
    } else if (src0->type == GGML_TYPE_Q1_0 && src1->type == GGML_TYPE_Q1_0) {
        ggml_cpy_q1_0_q1_0(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
+    } else if (src0->type == GGML_TYPE_Q2_0 && src1->type == GGML_TYPE_Q2_0) {
+        ggml_cpy_q2_0_q2_0(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
    } else if (src0->type == GGML_TYPE_MXFP4 && src1->type == GGML_TYPE_MXFP4) {
        ggml_cpy_mxfp4_mxfp4(src0_ddc, src1_ddc, ne, ne00, ne01, ne02, nb00, nb01, nb02, nb03, ne10, ne11, ne12, nb10, nb11, nb12, nb13, main_stream);
    } else if (src0->type == GGML_TYPE_NVFP4 && src1->type == GGML_TYPE_NVFP4) {
@@ -70,6 +70,39 @@ inline void cpy_blck_f32_q1_0(const char * cxi, char * cdsti) {
    }
 }

+inline int round_nearest_int(float x) {
+    return (int)(x >= 0.0f ? x + 0.5f : x - 0.5f);
+}
+
+inline void cpy_blck_f32_q2_0(const char * cxi, char * cdsti) {
+    const float * xi   = (const float *) cxi;
+    block_q2_0 *  dsti = (block_q2_0 *) cdsti;
+
+    float amax = 0.0f;
+
+    for (int j = 0; j < QK2_0; ++j) {
+        amax = sycl::fmax(amax, sycl::fabs((float) xi[j]));
+    }
+
+    const float d  = amax;
+    const float id = d > 0.0f ? 1.0f / d : 0.0f;
+
+    dsti->d = d;
+
+    for (int j = 0; j < QK2_0 / 4; ++j) {
+        dsti->qs[j] = 0;
+    }
+
+    for (int j = 0; j < QK2_0; ++j) {
+        int q = round_nearest_int(xi[j] * id) + 1;
+        q = dpct::max(0, dpct::min(3, q));
+
+        const int byte_index = j / 4;
+        const int bit_offset = (j % 4) * 2;
+        dsti->qs[byte_index] |= (uint8_t) q << bit_offset;
+    }
+}
+
 inline int best_index_mxfp4(const float x, const float e) {
    int best_index = 0;
    float best_err = sycl::fabs((float) (kvalues_mxfp4[0] * e - x));
@@ -25,6 +25,28 @@ typedef void (*dequantize_kernel_f32_t)(const void * vx, const int64_t ib, const
 static inline void get_scale_min_k4(int j, const uint8_t * q, uint8_t & d, uint8_t & m);
 #endif

+static __dpct_inline__ void dequantize_q2_0(const void *vx, const int64_t ib,
+                                            const int iqs, dfloat2 &v) {
+    const block_q2_0 * x = (const block_q2_0 *) vx;
+
+    const dfloat d = x[ib].d;
+
+    const int byte_idx = iqs / 4;
+    const int shift = (iqs % 4) * 2;
+    const uint8_t vui = x[ib].qs[byte_idx];
+
+    v.x() = (vui >> shift) & 3;
+    v.y() = (vui >> (shift + 2)) & 3;
+
+#ifdef GGML_SYCL_F16
+    v.s0() = ((dfloat)v.s0() - 1.0f) * d;
+    v.s1() = ((dfloat)v.s1() - 1.0f) * d;
+#else
+    v.x() = ((dfloat)v.x() - 1.0f) * d;
+    v.y() = ((dfloat)v.y() - 1.0f) * d;
+#endif // GGML_SYCL_F16
+}
+
 static __dpct_inline__ void dequantize_q4_0(const void *vx, const int64_t ib,
                                            const int iqs, dfloat2 &v) {
    const block_q4_0 * x = (const block_q4_0 *) vx;
@@ -0,0 +1,44 @@
+#include "fusion.hpp"
+
+bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops) {
+    if (!g_ggml_sycl_enable_fusion) {
+        return false;
+    }
+
+    if (!ggml_can_fuse(cgraph, node_idx, ops)) {
+        return false;
+    }
+
+    if (ops.size() == 2 && ops.begin()[0] == GGML_OP_RMS_NORM && ops.begin()[1] == GGML_OP_MUL) {
+        const ggml_tensor * rms_norm = cgraph->nodes[node_idx];
+        const ggml_tensor * mul      = cgraph->nodes[node_idx + 1];
+
+        GGML_ASSERT(rms_norm->src[0]->type == GGML_TYPE_F32);
+        GGML_ASSERT(rms_norm->type == GGML_TYPE_F32);
+
+        if (mul->src[0]->type != GGML_TYPE_F32 ||
+            mul->src[1]->type != GGML_TYPE_F32 ||
+            mul->type != GGML_TYPE_F32) {
+            return false;
+        }
+
+        // if rms norm is the B operand, then we don't handle broadcast
+        if (rms_norm == mul->src[1] && !ggml_are_same_shape(mul->src[0], rms_norm)) {
+            return false;
+        }
+
+        const ggml_tensor * mul_w = (mul->src[0] == rms_norm) ? mul->src[1] : mul->src[0];
+        // the fused kernel indexes the weight as mul[col], so it must span ncols contiguously
+        if (mul_w->ne[0] != rms_norm->ne[0] || mul_w->nb[0] != ggml_type_size(mul_w->type)) {
+            return false;
+        }
+
+        if (!ggml_is_contiguous_rows(mul->src[0]) || !ggml_is_contiguous_rows(mul->src[1])) {
+            return false;
+        }
+
+        return true;
+    }
+
+    return false;
+}
@@ -0,0 +1,15 @@
+#ifndef GGML_SYCL_FUSION_HPP
+#define GGML_SYCL_FUSION_HPP
+
+#include <initializer_list>
+
+#include "common.hpp"
+
+// Backend-side fusability test. `ops` names a candidate op sequence starting at cgraph node
+// `node_idx`; the result is true only if ggml considers that subgraph fusable *and* the SYCL
+// kernel which would service it accepts the tensors involved (types, shapes, contiguity).
+//
+// Lives in its own translation unit because it grows a branch per supported op sequence.
+bool ggml_sycl_can_fuse(const ggml_cgraph * cgraph, int node_idx, std::initializer_list<enum ggml_op> ops);
+
+#endif  // GGML_SYCL_FUSION_HPP
@@ -274,6 +274,8 @@ static const char* dev2dev_int2str(int dev2dev) {
        return "SYCL API";
    } else if (dev2dev == DEV2DEV_MEMCPY_L0) {
        return "Level Zero API";
+    } else if (dev2dev == DEV2DEV_MEMCPY_FORWARD) {
+        return "Host Forward";
    } else {
        return "Unknown";
    }
@@ -684,7 +686,11 @@ static void dev2dev_memcpy(int device_dst, sycl::queue &q_dst, int device_src, s
    }

    // Host-staged copy
-    GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward\n");
+    if(g_ggml_sycl_dev2dev_memcpy == DEV2DEV_MEMCPY_FORWARD) {
+        GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward for setting GGML_SYCL_DEV2DEV_MEMCPY=2\n");
+    } else {
+        GGML_SYCL_DEBUG("[SYCL] dev2dev memcpy by host forward for SYCL/L0 fallback\n");
+    }
    char *host_buf = (char *)malloc(size);
    q_src.memcpy(host_buf, (const char *)ptr_src, size).wait();
    q_dst.memcpy((char *)ptr_dst, host_buf, size).wait();
@@ -5398,6 +5404,13 @@ static void ggml_backend_sycl_graph_compute_impl(ggml_backend_sycl_context * syc
            }
        }
 #endif
+        if (node->op == GGML_OP_RMS_NORM &&
+            ggml_sycl_can_fuse(cgraph, i, { GGML_OP_RMS_NORM, GGML_OP_MUL })) {
+            ggml_sycl_op_rms_norm_fused(*sycl_ctx, node, cgraph->nodes[i + 1]);
+            i++;
+            continue;
+        }
+
        bool ok = ggml_sycl_compute_forward(*sycl_ctx, node);
        if (!ok) {
            GGML_LOG_ERROR("%s: error: op not supported %s (%s)\n", __func__, node->name, ggml_op_name(node->op));
@@ -1254,6 +1254,66 @@ static void mul_mat_vec_q1_0_q8_1_sycl_switch_ncols(
    }
 }

+static void mul_mat_vec_q2_0_q8_1_sycl(const void * vx, const void * vy,
+                                       float * dst, const int ncols,
+                                       const int nrows,
+                                       dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK2_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_vec_q<QK2_0, QI2_0, block_q2_0,
+                              VDR_Q2_0_Q8_1_MMVQ, vec_dot_q2_0_q8_1>(
+                    vx, vy, dst, ncols, nrows, item_ct1);
+            });
+    });
+}
+
+template <int ncols_dst>
+static void mul_mat_vec_q2_0_q8_1_sycl_ncols(
+        const void * vx, const void * vy, float * dst,
+        const int ncols, const int nrows,
+        const int stride_col_y, const int stride_col_dst,
+        dpct::queue_ptr stream) {
+    GGML_ASSERT(ncols % QK2_0 == 0);
+    const int block_num_y = (nrows + GGML_SYCL_MMV_Y - 1) / GGML_SYCL_MMV_Y;
+    const sycl::range<3> block_nums(1, 1, block_num_y);
+    const sycl::range<3> block_dims(1, GGML_SYCL_MMV_Y, WARP_SIZE);
+
+    stream->submit([&](sycl::handler & cgh) {
+        cgh.parallel_for(
+            sycl::nd_range<3>(block_nums * block_dims, block_dims),
+            [=](sycl::nd_item<3> item_ct1) [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                mul_mat_vec_q_ncols<QK2_0, QI2_0, block_q2_0,
+                                    VDR_Q2_0_Q8_1_MMVQ, vec_dot_q2_0_q8_1, ncols_dst>(
+                    vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, item_ct1);
+            });
+    });
+}
+
+static void mul_mat_vec_q2_0_q8_1_sycl_switch_ncols(
+        const void * vx, const void * vy, float * dst,
+        const int ncols, const int nrows, const int ncols_dst,
+        const int stride_col_y, const int stride_col_dst,
+        dpct::queue_ptr stream) {
+    switch (ncols_dst) {
+        case 1: mul_mat_vec_q2_0_q8_1_sycl(vx, vy, dst, ncols, nrows, stream); break;
+        case 2: mul_mat_vec_q2_0_q8_1_sycl_ncols<2>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 3: mul_mat_vec_q2_0_q8_1_sycl_ncols<3>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 4: mul_mat_vec_q2_0_q8_1_sycl_ncols<4>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 5: mul_mat_vec_q2_0_q8_1_sycl_ncols<5>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 6: mul_mat_vec_q2_0_q8_1_sycl_ncols<6>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 7: mul_mat_vec_q2_0_q8_1_sycl_ncols<7>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        case 8: mul_mat_vec_q2_0_q8_1_sycl_ncols<8>(vx, vy, dst, ncols, nrows, stride_col_y, stride_col_dst, stream); break;
+        default: GGML_ABORT("unsupported ncols_dst=%d for Q2_0 multi-col MMVQ", ncols_dst);
+    }
+}
+
 static void mul_mat_vec_q2_K_q8_1_sycl(const void *vx, const void *vy,
                                       float *dst, const int ncols,
                                       const int nrows,
@@ -2194,6 +2254,20 @@ void ggml_sycl_op_mul_mat_vec_q(ggml_backend_sycl_context & ctx, const ggml_tens
                    mul_mat_vec_q1_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
                }
                break;
+            case GGML_TYPE_Q2_0:
+                if (i == 0 && src1_ncols > 1 && src1_ncols <= 8) {
+                    const int stride_col_y   = src1_padded_col_size / QK8_1;
+                    const int stride_col_dst = dst->ne[0];
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q2_0_q8_1_sycl_switch_ncols ncols=%d\n", (int)src1_ncols);
+                    mul_mat_vec_q2_0_q8_1_sycl_switch_ncols(
+                        src0_dd_i, src1_ddq_i, dst_dd_i, ne00, row_diff,
+                        src1_ncols, stride_col_y, stride_col_dst, stream);
+                    return;
+                } else if (i == 0 || src1_ncols == 1) {
+                    GGML_SYCL_DEBUG("Calling mul_mat_vec_q2_0_q8_1_sycl\n");
+                    mul_mat_vec_q2_0_q8_1_sycl(src0_dd_i, src1_ddq_i_bs, dst_dd_i_bs, ne00, row_diff, stream);
+                }
+                break;
            case GGML_TYPE_Q2_K:
                if (i == 0 && src1_ncols > 1 && src1_ncols <= 8) {
                    const int stride_col_y   = src1_padded_col_size / QK8_1;
@@ -2503,6 +2577,11 @@ bool ggml_sycl_mul_mat_vec_q_id(
                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
            return true;
+        case GGML_TYPE_Q2_0:
+            launch_mul_mat_vec_q_moe<QK2_0, QI2_0, block_q2_0, VDR_Q2_0_Q8_1_MMVQ, vec_dot_q2_0_q8_1>(
+                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
+                expert_weight_stride, dst_row_stride, src1_row_stride, stream);
+            return true;
        case GGML_TYPE_Q2_K:
            launch_mul_mat_vec_q_moe<QK_K, QI2_K, block_q2_K, VDR_Q2_K_Q8_1_MMVQ, vec_dot_q2_K_q8_1>(
                vx_base, vy, ids_dev, dst_base, ncols, nrows, n_experts_used,
@@ -147,10 +147,13 @@ static void group_norm_f32(const float* x, float* dst, const int group_size, con
    }
 }

+template <bool do_multiply = false>
 static void rms_norm_f32(const float* x, float* dst, const int ncols,
    const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample,
    const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample,
-    const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size) {
+    const float eps, const sycl::nd_item<3>& item_ct1, float* s_sum, int block_size,
+    const float* mul = nullptr, const int64_t mul_stride_row = 0, const int64_t mul_stride_channel = 0,
+    const int64_t mul_stride_sample = 0, const int mul_nrows = 0, const int mul_nchannels = 0, const int mul_nsamples = 0) {

    const int nrows = item_ct1.get_group_range(2);
    const int nchannels = item_ct1.get_group_range(1);
@@ -170,6 +173,12 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols,
    x   += src_offset;
    dst += dst_offset;

+    if constexpr (do_multiply) {
+        const int mul_row     = row     % mul_nrows;
+        const int mul_channel = channel % mul_nchannels;
+        const int mul_sample  = sample  % mul_nsamples;
+        mul += mul_sample * mul_stride_sample + mul_channel * mul_stride_channel + mul_row * mul_stride_row;
+    }

    float tmp = 0.0f; // partial sum for thread in warp

@@ -202,7 +211,11 @@ static void rms_norm_f32(const float* x, float* dst, const int ncols,
    const float scale = sycl::rsqrt(mean + eps);

    for (int col = tid; col < ncols; col += block_size) {
-        dst[col * dst_stride_col] = scale * x[col * src_stride_col];
+        if constexpr (do_multiply) {
+            dst[col * dst_stride_col] = scale * x[col * src_stride_col] * mul[col];
+        } else {
+            dst[col * dst_stride_col] = scale * x[col * src_stride_col];
+        }
    }
 }

@@ -376,6 +389,49 @@ static void rms_norm_f32_sycl(const float* x, float* dst, const int ncols, const
    }
 }

+static void rms_norm_mul_f32_sycl(const float* x, const float* mul, float* dst, const int ncols, const int nrows,
+        const int nchannels, const int nsamples,
+        const int64_t src_stride_col, const int64_t src_stride_row, const int64_t src_stride_channel, const int64_t src_stride_sample,
+        const int64_t dst_stride_col, const int64_t dst_stride_row, const int64_t dst_stride_channel, const int64_t dst_stride_sample,
+        const int64_t mul_stride_row, const int64_t mul_stride_channel, const int64_t mul_stride_sample,
+        const int mul_nrows, const int mul_nchannels, const int mul_nsamples,
+        const float eps, queue_ptr stream, int device) {
+    const sycl::range<3> global_dims(nsamples, nchannels, nrows);
+    if (ncols < 1024) {
+        const sycl::range<3> block_dims(1, 1, WARP_SIZE);
+        stream->submit([&](sycl::handler& cgh) {
+            cgh.parallel_for(
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32<true>(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1, nullptr, WARP_SIZE,
+                        mul, mul_stride_row, mul_stride_channel, mul_stride_sample, mul_nrows, mul_nchannels, mul_nsamples);
+                });
+            });
+    }
+    else {
+        const int work_group_size = ggml_sycl_info().max_work_group_sizes[device];
+        assert(work_group_size % (WARP_SIZE * WARP_SIZE) == 0);
+        const sycl::range<3> block_dims(1, 1, work_group_size);
+        stream->submit([&](sycl::handler& cgh) {
+            sycl::local_accessor<float, 1> s_sum_acc_ct1(sycl::range<1>(work_group_size / WARP_SIZE), cgh);
+            cgh.parallel_for(
+                sycl::nd_range<3>(global_dims * block_dims, block_dims),
+                [=](sycl::nd_item<3> item_ct1)
+                [[sycl::reqd_sub_group_size(WARP_SIZE)]] {
+                    rms_norm_f32<true>(x, dst, ncols,
+                        src_stride_col, src_stride_row, src_stride_channel, src_stride_sample,
+                        dst_stride_col, dst_stride_row, dst_stride_channel, dst_stride_sample,
+                        eps, item_ct1, get_pointer(s_sum_acc_ct1), work_group_size,
+                        mul, mul_stride_row, mul_stride_channel, mul_stride_sample, mul_nrows, mul_nchannels, mul_nsamples);
+                });
+            });
+    }
+}
+
 template<int warp_size>
 static void l2_norm_f32_sycl(const float *   x,
                             float *         dst,
@@ -518,6 +574,66 @@ void ggml_sycl_op_rms_norm(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
        ss0, ss1, ss2, ss3, ds0, ds1, ds2, ds3, eps, main_stream, ctx.device);
 }

+void ggml_sycl_op_rms_norm_fused(ggml_backend_sycl_context & ctx, ggml_tensor * dst, ggml_tensor * mul_tensor) {
+    const ggml_tensor * rms_norm_src = dst->src[0];
+    float eps = 0.0f;
+    memcpy(&eps, dst->op_params, sizeof(float));
+
+    const float *       src0_dd = static_cast<const float *>(rms_norm_src->data);
+    const float *       mul_dd  = nullptr;
+    const ggml_tensor * mul_src = nullptr;
+    if (mul_tensor->src[0] == dst) {
+        mul_dd  = static_cast<const float *>(mul_tensor->src[1]->data);
+        mul_src = mul_tensor->src[1];
+    } else if (mul_tensor->src[1] == dst) {
+        mul_dd  = static_cast<const float *>(mul_tensor->src[0]->data);
+        mul_src = mul_tensor->src[0];
+    } else {
+        GGML_ASSERT(false);
+    }
+    float * dst_dd = static_cast<float *>(mul_tensor->data);
+
+    dpct::queue_ptr main_stream = ctx.stream();
+    SYCL_CHECK(ggml_sycl_set_device(ctx.device));
+
+    GGML_ASSERT(rms_norm_src->type == GGML_TYPE_F32);
+    GGML_ASSERT(dst->type == GGML_TYPE_F32);
+    GGML_ASSERT(mul_tensor->type == GGML_TYPE_F32);
+    GGML_ASSERT(eps >= 0.0f);
+
+    const int64_t ne00 = rms_norm_src->ne[0];
+    const int64_t ne01 = rms_norm_src->ne[1];
+    const int64_t ne02 = rms_norm_src->ne[2];
+    const int64_t ne03 = rms_norm_src->ne[3];
+
+    const size_t ts0 = ggml_type_size(rms_norm_src->type);
+    GGML_ASSERT(rms_norm_src->nb[0] == ts0);
+    const int64_t s00 = rms_norm_src->nb[0] / ts0;
+    const int64_t s01 = rms_norm_src->nb[1] / ts0;
+    const int64_t s02 = rms_norm_src->nb[2] / ts0;
+    const int64_t s03 = rms_norm_src->nb[3] / ts0;
+
+    const size_t tdst = ggml_type_size(mul_tensor->type);
+    GGML_ASSERT(mul_tensor->nb[0] == tdst);
+    const int64_t d00 = mul_tensor->nb[0] / tdst;
+    const int64_t d01 = mul_tensor->nb[1] / tdst;
+    const int64_t d02 = mul_tensor->nb[2] / tdst;
+    const int64_t d03 = mul_tensor->nb[3] / tdst;
+
+    const size_t ts_mul = ggml_type_size(mul_src->type);
+    GGML_ASSERT(mul_src->nb[0] == ts_mul);
+    const int64_t mul_s01 = mul_src->nb[1] / ts_mul;
+    const int64_t mul_s02 = mul_src->nb[2] / ts_mul;
+    const int64_t mul_s03 = mul_src->nb[3] / ts_mul;
+    const int mul_nrows     = mul_src->ne[1];
+    const int mul_nchannels = mul_src->ne[2];
+    const int mul_nsamples  = mul_src->ne[3];
+
+    rms_norm_mul_f32_sycl(src0_dd, mul_dd, dst_dd, ne00, ne01, ne02, ne03,
+        s00, s01, s02, s03, d00, d01, d02, d03,
+        mul_s01, mul_s02, mul_s03, mul_nrows, mul_nchannels, mul_nsamples, eps, main_stream, ctx.device);
+}
+
 void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context & ctx, ggml_tensor * dst) {
    scope_op_debug_print scope_dbg_print(__func__, dst, /*num_src=*/2);

@@ -19,6 +19,8 @@ void ggml_sycl_op_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst);

 void ggml_sycl_op_rms_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst);

+void ggml_sycl_op_rms_norm_fused(ggml_backend_sycl_context& ctx, ggml_tensor* dst, ggml_tensor* mul);
+
 void ggml_sycl_op_rms_norm_back(ggml_backend_sycl_context& ctx, ggml_tensor* dst);

 void ggml_sycl_op_group_norm(ggml_backend_sycl_context& ctx, ggml_tensor* dst);
@@ -658,6 +658,40 @@ template <> struct reorder_vec_dot_q_sycl<GGML_TYPE_Q6_K> {
 #define VDR_Q4_0_Q8_1_MMVQ 2
 #define VDR_Q4_0_Q8_1_MMQ  4

+#define VDR_Q2_0_Q8_1_MMVQ 1
+
+template <int vdr>
+static __dpct_inline__ float vec_dot_q2_0_q8_1_impl(
+    const int * v,
+    const int * u,
+    const float & d2,
+    const sycl::half2 & ds8) {
+    int sumi = 0;
+
+#pragma unroll
+    for (int i = 0; i < vdr; ++i) {
+#pragma unroll
+        for (int j = 0; j < 4; ++j) {
+            const uint8_t q = (uint8_t) ((uint32_t) v[i] >> (8 * j));
+
+            // unpack 2-bit values to byte lanes (0..3), then apply zero-point
+            // correction with ds8f.y() below, mirroring the q4_0 style.
+            int vi = 0;
+            vi |= (((q >> 0) & 0x3) & 0xFF) << 0;
+            vi |= (((q >> 2) & 0x3) & 0xFF) << 8;
+            vi |= (((q >> 4) & 0x3) & 0xFF) << 16;
+            vi |= (((q >> 6) & 0x3) & 0xFF) << 24;
+
+            sumi = dpct::dp4a(vi, u[4 * i + j], sumi);
+        }
+    }
+
+    const sycl::float2 ds8f = ds8.convert<float, sycl::rounding_mode::automatic>();
+    // q2_0 has zero-point 1. Scale ds8f.y() by processed-lane ratio,
+    // consistent with q4_0's explicit zero-point subtraction style.
+    return d2 * (sumi * ds8f.x() - ((float) vdr / (float) QI2_0) * ds8f.y());
+}
+
 template <int vdr>
 static __dpct_inline__ float vec_dot_q4_0_q8_1_impl(const int * v, const int * u, const float & d4,
                                                    const sycl::half2 & ds8) {
@@ -882,6 +916,41 @@ vec_dot_q4_0_q8_1(const void *__restrict__ vbq,
    return vec_dot_q4_0_q8_1_impl<VDR_Q4_0_Q8_1_MMVQ>(v, u, bq4_0->d, bq8_1->ds);
 }

+static __dpct_inline__ float
+vec_dot_q2_0_q8_1(const void *__restrict__ vbq,
+                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
+
+    const block_q2_0 * bq2_0 = (const block_q2_0 *) vbq;
+
+    int v[2 * VDR_Q2_0_Q8_1_MMVQ];
+    int u[8 * VDR_Q2_0_Q8_1_MMVQ];
+
+#pragma unroll
+    for (int i = 0; i < VDR_Q2_0_Q8_1_MMVQ; ++i) {
+        const int base = 4 * (iqs + i);
+
+        // Q2_0 has QK2_0 = 64 and uses 2 x QK8_1 blocks on the RHS.
+        v[2 * i + 0] = get_int_from_uint8(bq2_0->qs, iqs + i);
+        v[2 * i + 1] = get_int_from_uint8(bq2_0->qs, iqs + i + QI2_0);
+
+        u[8 * i + 0] = get_int_from_int8_aligned(bq8_1[0].qs, base + 0);
+        u[8 * i + 1] = get_int_from_int8_aligned(bq8_1[0].qs, base + 1);
+        u[8 * i + 2] = get_int_from_int8_aligned(bq8_1[0].qs, base + 2);
+        u[8 * i + 3] = get_int_from_int8_aligned(bq8_1[0].qs, base + 3);
+
+        u[8 * i + 4] = get_int_from_int8_aligned(bq8_1[1].qs, base + 0);
+        u[8 * i + 5] = get_int_from_int8_aligned(bq8_1[1].qs, base + 1);
+        u[8 * i + 6] = get_int_from_int8_aligned(bq8_1[1].qs, base + 2);
+        u[8 * i + 7] = get_int_from_int8_aligned(bq8_1[1].qs, base + 3);
+    }
+
+    const float sum0 = vec_dot_q2_0_q8_1_impl<VDR_Q2_0_Q8_1_MMVQ>(
+        v + 0, u + 0, bq2_0->d, bq8_1[0].ds);
+    const float sum1 = vec_dot_q2_0_q8_1_impl<VDR_Q2_0_Q8_1_MMVQ>(
+        v + VDR_Q2_0_Q8_1_MMVQ, u + 4 * VDR_Q2_0_Q8_1_MMVQ, bq2_0->d, bq8_1[1].ds);
+    return sum0 + sum1;
+}
+
 static __dpct_inline__ float
 vec_dot_q4_1_q8_1(const void *__restrict__ vbq,
                  const block_q8_1 *__restrict__ bq8_1, const int &iqs) {
@@ -1481,6 +1481,11 @@ struct vk_op_binary_push_constants {
    float param1; float param2; int32_t param3;
 };

+// Distinct type with the same layout so concat can overload tensor offset initialization.
+struct vk_op_concat_push_constants : vk_op_binary_push_constants {};
+static_assert(sizeof(vk_op_concat_push_constants) == sizeof(vk_op_binary_push_constants));
+static_assert(std::is_standard_layout_v<vk_op_concat_push_constants>);
+
 struct vk_op_multi_add_push_constants {
    // shape for dst
    uint32_t ne20; uint32_t ne21; uint32_t ne22; uint32_t ne23;
@@ -2246,6 +2251,40 @@ static uint32_t get_misalign_bytes(const ggml_backend_vk_context * ctx, const gg
    return ((vk_tensor_offset(t) + t->view_offs) & (ctx->device->properties.limits.minStorageBufferOffsetAlignment - 1));;
 }

+static uint32_t ggml_vk_concat_unit_size(ggml_type type) {
+    const uint32_t type_size = ggml_type_size(type);
+
+    if (!ggml_is_quantized(type)) {
+        return type_size;
+    }
+
+    // Use the widest existing concat shader that evenly divides a quant block.
+    if (type_size % 8 == 0) {
+        return 8;
+    }
+    if (type_size % 4 == 0) {
+        return 4;
+    }
+    if (type_size % 2 == 0) {
+        return 2;
+    }
+    return 1;
+}
+
+static bool ggml_vk_concat_supported(const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * dst) {
+    if (src0->type != src1->type || src0->type != dst->type) {
+        return false;
+    }
+
+    if (!ggml_is_quantized(src0->type)) {
+        const size_t type_size = ggml_type_size(src0->type);
+        return type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8;
+    }
+
+    // Quantized tensor rows are block-aligned when created.
+    return ggml_is_contiguous_rows(src0) && ggml_is_contiguous_rows(src1) && ggml_is_contiguous_rows(dst);
+}
+
 template <typename T> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, T &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    GGML_UNUSED(p);
    GGML_UNUSED(src0);
@@ -10896,14 +10935,10 @@ static vk_pipeline ggml_vk_op_get_pipeline(ggml_backend_vk_context * ctx, const
        }
        return nullptr;
    case GGML_OP_CONCAT: {
-        if (src0->type != src1->type || src0->type != dst->type) {
+        if (!ggml_vk_concat_supported(src0, src1, dst)) {
            return nullptr;
        }
-        if (ggml_blck_size(src0->type) != 1) {
-            return nullptr;
-        }
-        const size_t type_size = ggml_type_size(src0->type);
-        switch (type_size) {
+        switch (ggml_vk_concat_unit_size(src0->type)) {
        case 1:
            return ctx->device->pipeline_concat_i8;
        case 2:
@@ -11595,6 +11630,18 @@ template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk
    GGML_UNUSED(src3);
 }

+template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_concat_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
+    const uint32_t unit_size = ggml_vk_concat_unit_size(dst->type);
+    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / unit_size;
+    const uint32_t b_offset = get_misalign_bytes(ctx, src1) / unit_size;
+    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / unit_size;
+
+    p.misalign_offsets = (a_offset << 16) | (b_offset << 8) | d_offset;
+
+    GGML_UNUSED(src2);
+    GGML_UNUSED(src3);
+}
+
 template <> void init_pushconst_tensor_offsets(ggml_backend_vk_context * ctx, vk_op_upscale_push_constants &p, const ggml_tensor * src0, const ggml_tensor * src1, const ggml_tensor * src2, const ggml_tensor * src3, ggml_tensor * dst) {
    const uint32_t a_offset = get_misalign_bytes(ctx, src0) / ggml_type_size(src0->type);
    const uint32_t d_offset = get_misalign_bytes(ctx, dst) / ggml_type_size(dst->type);
@@ -11630,7 +11677,7 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
    }
    std::cerr << "), (" << dst << ", name=" << dst->name << ", type=" << dst->type << ", ne0=" << dst->ne[0] << ", ne1=" << dst->ne[1] << ", ne2=" << dst->ne[2] << ", ne3=" << dst->ne[3] << ", nb0=" << dst->nb[0] << ", nb1=" << dst->nb[1] << ", nb2=" << dst->nb[2] << ", nb3=" << dst->nb[3];
    std::cerr << "), " << ggml_op_name(op) << ")");
-    GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
+    GGML_ASSERT(op == GGML_OP_GET_ROWS || op == GGML_OP_CPY || op == GGML_OP_CONCAT || (!ggml_is_quantized(src0->type) && (src1 == nullptr || !ggml_is_quantized(src1->type))));  // NOLINT
    GGML_ASSERT(dst->buffer != nullptr);
    const uint64_t ne00 = src0->ne[0];
    const uint64_t ne01 = src0->ne[1];
@@ -11885,6 +11932,9 @@ static void ggml_vk_op_f32(ggml_backend_vk_context * ctx, vk_context& subctx, co
                    ne *= ggml_type_size(src0->type) / 2;
                }
            }
+            if (op == GGML_OP_CONCAT && ggml_is_quantized(dst->type)) {
+                ne = ne / ggml_blck_size(dst->type) * ggml_type_size(dst->type) / ggml_vk_concat_unit_size(dst->type);
+            }
            // copy_to_quant has block size of 32, and each thread does QUANT_K elements.
            // Splitting into 512x512xZ wouldn't work well since each workgroup does 1024 elements.
            // So divide by block size here before splitting into 512x512 groups.
@@ -12525,18 +12575,28 @@ static void ggml_vk_opt_step_sgd(ggml_backend_vk_context * ctx, vk_context& subc
 static void ggml_vk_concat(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, const ggml_tensor * src1, ggml_tensor * dst) {
    int * op_params = (int *)dst->op_params;

-    const uint32_t src0_type_size = ggml_type_size(src0->type);
-    const uint32_t src1_type_size = ggml_type_size(src1->type);
-    const uint32_t dst_type_size = ggml_type_size(dst->type);
+    const uint32_t unit_size = ggml_vk_concat_unit_size(dst->type);
+    const uint32_t units_per_block = ggml_type_size(dst->type) / unit_size;
+    const uint32_t block_size = ggml_blck_size(dst->type);
+    const bool quantized = ggml_is_quantized(dst->type);

-    ggml_vk_op_f32<vk_op_binary_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, {
-        (uint32_t)ggml_nelements(dst),
-        (uint32_t)src0->ne[0], (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], (uint32_t)src0->nb[0] / src0_type_size, (uint32_t)src0->nb[1] / src0_type_size, (uint32_t)src0->nb[2] / src0_type_size, (uint32_t)src0->nb[3] / src0_type_size,
-        (uint32_t)src1->ne[0], (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], (uint32_t)src1->nb[0] / src1_type_size, (uint32_t)src1->nb[1] / src1_type_size, (uint32_t)src1->nb[2] / src1_type_size, (uint32_t)src1->nb[3] / src1_type_size,
-        (uint32_t) dst->ne[0], (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], (uint32_t) dst->nb[0] /  dst_type_size, (uint32_t) dst->nb[1] /  dst_type_size, (uint32_t) dst->nb[2] /  dst_type_size, (uint32_t) dst->nb[3] /  dst_type_size,
+    // Address dimension 0 in packed storage units; higher strides may be noncontiguous.
+    const uint32_t ne00 = src0->ne[0] / block_size * units_per_block;
+    const uint32_t ne10 = src1->ne[0] / block_size * units_per_block;
+    const uint32_t ne20 =  dst->ne[0] / block_size * units_per_block;
+    const uint32_t nb00 = quantized ? 1 : src0->nb[0] / unit_size;
+    const uint32_t nb10 = quantized ? 1 : src1->nb[0] / unit_size;
+    const uint32_t nb20 = quantized ? 1 :  dst->nb[0] / unit_size;
+
+    vk_op_concat_push_constants pc {{
+        ne20 * (uint32_t)dst->ne[1] * (uint32_t)dst->ne[2] * (uint32_t)dst->ne[3],
+        ne00, (uint32_t)src0->ne[1], (uint32_t)src0->ne[2],(uint32_t)src0->ne[3], nb00, (uint32_t)src0->nb[1] / unit_size, (uint32_t)src0->nb[2] / unit_size, (uint32_t)src0->nb[3] / unit_size,
+        ne10, (uint32_t)src1->ne[1], (uint32_t)src1->ne[2],(uint32_t)src1->ne[3], nb10, (uint32_t)src1->nb[1] / unit_size, (uint32_t)src1->nb[2] / unit_size, (uint32_t)src1->nb[3] / unit_size,
+        ne20, (uint32_t) dst->ne[1], (uint32_t) dst->ne[2],(uint32_t) dst->ne[3], nb20, (uint32_t) dst->nb[1] / unit_size, (uint32_t) dst->nb[2] / unit_size, (uint32_t) dst->nb[3] / unit_size,
        0,
        0.0f, 0.0f, op_params[0],
-    });
+    }};
+    ggml_vk_op_f32<vk_op_concat_push_constants>(ctx, subctx, src0, src1, nullptr, nullptr, dst, GGML_OP_CONCAT, std::move(pc));
 }

 static void ggml_vk_upscale(ggml_backend_vk_context * ctx, vk_context& subctx, const ggml_tensor * src0, ggml_tensor * dst) {
@@ -17872,12 +17932,7 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
            return op->src[0]->type == op->src[1]->type && op->src[0]->type == op->type &&
                   (op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_I32);
        case GGML_OP_CONCAT: {
-            if (op->src[0]->type != op->src[1]->type || op->src[0]->type != op->type) {
-                return false;
-            }
-            const size_t type_size = ggml_type_size(op->type);
-            return ggml_blck_size(op->type) == 1 &&
-                   (type_size == 1 || type_size == 2 || type_size == 4 || type_size == 8);
+            return ggml_vk_concat_supported(op->src[0], op->src[1], op);
        }
        case GGML_OP_ADD1:
            return (op->src[0]->type == GGML_TYPE_F32 && op->src[1]->type == GGML_TYPE_F32)
@@ -18016,10 +18071,17 @@ static bool ggml_backend_vk_device_supports_op(ggml_backend_dev_t dev, const ggm
        case GGML_OP_CONV_2D:
        case GGML_OP_CONV_TRANSPOSE_2D:
            {
+                const bool transpose = op->op == GGML_OP_CONV_TRANSPOSE_2D;
+                const int64_t cout = !transpose ? op->src[0]->ne[3] : op->src[0]->ne[2];
+                const int64_t cin  = !transpose ? op->src[0]->ne[2] : op->src[0]->ne[3];
+
                // Channel-contiguous format is not supported yet.
                return ((op->src[0]->type == GGML_TYPE_F32 || op->src[0]->type == GGML_TYPE_F16) &&
+                    (op->src[0]->nb[0] == sizeof(float) || op->src[0]->nb[0] == sizeof(ggml_fp16_t) ) &&
                    op->src[1]->type == GGML_TYPE_F32 &&
                    op->type == GGML_TYPE_F32 &&
+                    cout == op->ne[2] &&
+                    cin == op->src[1]->ne[2] &&
                    ggml_is_contiguous(op->src[0]) &&
                    ggml_is_contiguous(op->src[1]) &&
                    ggml_is_contiguous(op));
@@ -591,7 +591,8 @@ struct ggml_webgpu_flash_attn_common_pipeline_key {
    ggml_type dst_type;
    uint32_t  head_dim_qk;
    uint32_t  head_dim_v;
-    bool      kv_direct;
+    bool      k_direct;
+    bool      v_direct;
    bool      kv_overlap;
    bool      has_mask;
    bool      has_sinks;
@@ -600,8 +601,9 @@ struct ggml_webgpu_flash_attn_common_pipeline_key {
    bool operator==(const ggml_webgpu_flash_attn_common_pipeline_key & other) const {
        return q_type == other.q_type && k_type == other.k_type && v_type == other.v_type &&
               dst_type == other.dst_type && head_dim_qk == other.head_dim_qk && head_dim_v == other.head_dim_v &&
-               kv_direct == other.kv_direct && kv_overlap == other.kv_overlap && has_mask == other.has_mask &&
-               has_sinks == other.has_sinks && uses_logit_softcap == other.uses_logit_softcap;
+               k_direct == other.k_direct && v_direct == other.v_direct && kv_overlap == other.kv_overlap &&
+               has_mask == other.has_mask && has_sinks == other.has_sinks &&
+               uses_logit_softcap == other.uses_logit_softcap;
    }
 };

@@ -613,7 +615,8 @@ inline void ggml_webgpu_flash_attn_hash_common_pipeline_key(size_t &
    ggml_webgpu_hash_combine(seed, key.dst_type);
    ggml_webgpu_hash_combine(seed, key.head_dim_qk);
    ggml_webgpu_hash_combine(seed, key.head_dim_v);
-    ggml_webgpu_hash_combine(seed, key.kv_direct);
+    ggml_webgpu_hash_combine(seed, key.k_direct);
+    ggml_webgpu_hash_combine(seed, key.v_direct);
    ggml_webgpu_hash_combine(seed, key.kv_overlap);
    ggml_webgpu_hash_combine(seed, key.has_mask);
    ggml_webgpu_hash_combine(seed, key.has_sinks);
@@ -687,12 +690,13 @@ inline bool ggml_webgpu_flash_attn_float_vec4_aligned(const ggml_tensor * K,
           ggml_webgpu_flash_attn_float_vec4_aligned(V, storage_offset_alignment);
 }

-inline bool ggml_webgpu_flash_attn_kv_direct(const ggml_tensor * Q,
-                                             const ggml_tensor * K,
-                                             const ggml_tensor * V,
-                                             uint32_t            kv_direct_align) {
-    return K->type == GGML_TYPE_F16 && V->type == GGML_TYPE_F16 && (Q->ne[0] % kv_direct_align == 0) &&
-           (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
+inline bool ggml_webgpu_flash_attn_k_direct(const ggml_tensor * Q, const ggml_tensor * K, uint32_t kv_direct_align) {
+    return (K->type == GGML_TYPE_F16 || K->type == GGML_TYPE_Q8_0 || K->type == GGML_TYPE_Q4_0) &&
+           (Q->ne[0] % kv_direct_align == 0) && (K->ne[1] % GGML_WEBGPU_KV_SEQ_PAD == 0);
+}
+
+inline bool ggml_webgpu_flash_attn_v_direct(const ggml_tensor * Q, const ggml_tensor * V, uint32_t kv_direct_align) {
+    return ggml_webgpu_flash_attn_k_direct(Q, V, kv_direct_align);
 }

 inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_common_pipeline_key(
@@ -706,10 +710,11 @@ inline ggml_webgpu_flash_attn_common_pipeline_key ggml_webgpu_flash_attn_make_co
    key.dst_type                                   = context.dst->type;
    key.head_dim_qk                                = (uint32_t) context.src0->ne[0];
    key.head_dim_v                                 = (uint32_t) context.src2->ne[0];
-    key.kv_direct  = ggml_webgpu_flash_attn_kv_direct(context.src0, context.src1, context.src2, kv_direct_align);
-    key.kv_overlap = kv_overlap;
-    key.has_mask   = context.src3 != nullptr;
-    key.has_sinks  = context.src4 != nullptr;
+    key.k_direct           = ggml_webgpu_flash_attn_k_direct(context.src0, context.src1, kv_direct_align);
+    key.v_direct           = ggml_webgpu_flash_attn_v_direct(context.src0, context.src2, kv_direct_align);
+    key.kv_overlap         = kv_overlap;
+    key.has_mask           = context.src3 != nullptr;
+    key.has_sinks          = context.src4 != nullptr;
    key.uses_logit_softcap = ggml_get_op_params_f32(context.dst, 2) != 0.0f;
    return key;
 }
@@ -794,9 +799,13 @@ inline std::vector<std::string> ggml_webgpu_flash_attn_common_defines(
        defines.push_back("LOGIT_SOFTCAP");
        variant += "_lgsc";
    }
-    if (key.kv_direct) {
-        defines.push_back("KV_DIRECT");
-        variant += "_kvdirect";
+    if (key.k_direct) {
+        defines.push_back("K_DIRECT");
+        variant += "_k_direct";
+    }
+    if (key.v_direct) {
+        defines.push_back("V_DIRECT");
+        variant += "_v_direct";
    }
    if (key.kv_overlap) {
        defines.push_back("KV_OVERLAP");
@@ -815,6 +824,12 @@ inline std::vector<std::string> ggml_webgpu_flash_attn_common_defines(

    if (ggml_is_quantized(key.k_type) || ggml_is_quantized(key.v_type)) {
        defines.push_back("U32_DEQUANT_HELPERS");
+        if (ggml_is_quantized(key.k_type)) {
+            defines.push_back("LOADERS_QUANTIZED_K");
+        }
+        if (ggml_is_quantized(key.v_type)) {
+            defines.push_back("LOADERS_QUANTIZED_V");
+        }
    }

    return defines;
@@ -2792,12 +2807,14 @@ class ggml_webgpu_shader_lib {
        ggml_webgpu_flash_attn_pipeline_key key = {};
        key.common                              = ggml_webgpu_flash_attn_make_common_pipeline_key(
            context, decisions.use_sg_matrix ? context.sg_mat_k : 1u, kv_overlap);
-        key.common.kv_direct = decisions.use_sg_matrix && key.common.kv_direct;
-        key.use_sg_matrix    = decisions.use_sg_matrix;
+        key.common.k_direct &= decisions.use_sg_matrix && key.common.k_type == GGML_TYPE_F16;
+        key.common.v_direct &= decisions.use_sg_matrix && key.common.v_type == GGML_TYPE_F16;
+        key.use_sg_matrix = decisions.use_sg_matrix;

        const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(
            context.wg_mem_limit_bytes, decisions.q_tile, decisions.use_sg_matrix ? context.sg_mat_n : 1u,
-            key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask, key.common.kv_direct);
+            key.common.head_dim_qk, key.common.head_dim_v, key.common.has_mask,
+            key.common.k_direct || key.common.v_direct);
        GGML_ASSERT(max_kv_tile > 0);

        decisions.kv_tile = decisions.use_sg_matrix ?
@@ -2809,7 +2826,7 @@ class ggml_webgpu_shader_lib {
                std::min(context.max_wg_size, std::max(GGML_WEBGPU_FLASH_ATTN_PREFERRED_WG_SIZE,
                                                       GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE * context.max_subgroup_size));

-        if (key.common.kv_direct) {
+        if (key.common.k_direct || key.common.v_direct) {
            decisions.kv_tile = std::min(decisions.kv_tile, GGML_WEBGPU_KV_SEQ_PAD);
            while (GGML_WEBGPU_KV_SEQ_PAD % decisions.kv_tile != 0) {
                decisions.kv_tile -= decisions.use_sg_matrix ? context.sg_mat_n : context.min_subgroup_size;
@@ -2856,9 +2873,9 @@ class ggml_webgpu_shader_lib {
        }

        ggml_webgpu_flash_attn_vec_decisions decisions = {};
-        decisions.kv_tile =
-            ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk,
-                                                   key.common.head_dim_v, key.common.has_mask, key.common.kv_direct);
+        decisions.kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile(context.wg_mem_limit_bytes, key.common.head_dim_qk,
+                                                                   key.common.head_dim_v, key.common.has_mask,
+                                                                   key.common.k_direct || key.common.v_direct);
        decisions.wg_size = context.max_subgroup_size;

        std::string              variant = "flash_attn_vec";
@@ -2870,12 +2887,10 @@ class ggml_webgpu_shader_lib {
            variant += "_mask_blk";
        }

-        uint32_t d_split = context.min_subgroup_size;
-        if (key.common.k_type == GGML_TYPE_F16 && key.common.v_type == GGML_TYPE_F16) {
-            const uint32_t D     = key.common.head_dim_qk | key.common.head_dim_v;
-            const uint32_t D_lsb = D & (~(D - 1u));
-            d_split              = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u));
-        }
+        uint32_t       d_split = context.min_subgroup_size;
+        const uint32_t D       = key.common.head_dim_qk | key.common.head_dim_v;
+        const uint32_t D_lsb   = D & (~(D - 1u));
+        d_split                = std::min(std::min(context.min_subgroup_size, 4u), std::max(D_lsb / 4u, 1u));

        defines.push_back(std::string("D_SPLIT=") + std::to_string(d_split));
        variant += "_dsplit" + std::to_string(d_split);
@@ -3839,7 +3839,8 @@ static size_t ggml_backend_webgpu_buffer_type_get_alloc_size(ggml_backend_buffer
                const auto &        capabilities = ctx->webgpu_global_ctx->capabilities;
                if (ggml_webgpu_flash_attn_use_vec_path(ctx->webgpu_global_ctx, Q, K, V)) {
                    const bool kv_direct =
-                        ggml_webgpu_flash_attn_kv_direct(Q, K, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH);
+                        ggml_webgpu_flash_attn_k_direct(Q, K, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH) ||
+                        ggml_webgpu_flash_attn_v_direct(Q, V, GGML_WEBGPU_FLASH_ATTN_TILE_KV_VEC_WIDTH);
                    const uint32_t kv_tile = ggml_webgpu_flash_attn_get_vec_kv_tile(
                        capabilities.limits.maxComputeWorkgroupStorageSize, (uint32_t) Q->ne[0], (uint32_t) V->ne[0],
                        mask != nullptr, kv_direct);
@@ -4448,9 +4449,10 @@ static bool ggml_backend_webgpu_device_supports_op(ggml_backend_dev_t dev, const
                const uint32_t q_tile =
                    use_subgroup_matrix ? capabilities.sg_mat_m : GGML_WEBGPU_FLASH_ATTN_TILE_Q_TILE;
                const uint32_t kv_granularity = use_subgroup_matrix ? capabilities.sg_mat_n : 1u;
-                const bool kv_direct = use_subgroup_matrix ?
-                                           ggml_webgpu_flash_attn_kv_direct(src0, src1, src2, capabilities.sg_mat_k) :
-                                           false;
+                const bool     kv_direct = use_subgroup_matrix ?
+                                               ggml_webgpu_flash_attn_k_direct(src0, src1, capabilities.sg_mat_k) ||
+                                                   ggml_webgpu_flash_attn_v_direct(src0, src2, capabilities.sg_mat_k) :
+                                               false;
                const uint32_t max_kv_tile = ggml_webgpu_flash_attn_max_kv_tile(
                    capabilities.limits.maxComputeWorkgroupStorageSize, q_tile, kv_granularity, (uint32_t) src0->ne[0],
                    (uint32_t) src2->ne[0], op->src[3] != nullptr, kv_direct);
@@ -9,6 +9,12 @@ fn get_byte_i32(value: u32, index: u32) -> i32 {
 #endif

 #ifdef U32_DEQUANT_HELPERS
+
+fn f16_from_u16(bits: u32) -> f16 {
+    let packed = unpack2x16float(bits);
+    return f16(packed[0]);
+}
+
 #ifdef DECLARE_BYTE_LOADERS_SRC
 fn load_u16_at_src(byte_offset: u32) -> u32 {
    let word = src[byte_offset / 4u];
@@ -36,7 +42,7 @@ fn load_f16_as_f32_at_src(byte_offset: u32) -> f32 {
    let d_bits = (word >> shift) & 0xFFFFu;
    return unpack2x16float(d_bits)[0];
 }
-#endif
+#endif // DECLARE_BYTE_LOADERS_SRC

 #ifdef DECLARE_BYTE_LOADERS_SRC0
 fn load_u16_at_src0(byte_offset: u32) -> u32 {
@@ -72,8 +78,47 @@ fn load_f16_as_f32_at_src0(byte_offset: u32) -> f32 {
    let d_bits = (word >> shift) & 0xFFFFu;
    return unpack2x16float(d_bits)[0];
 }
-#endif
-#endif
+#endif // DECLARE_BYTE_LOADERS_SRC0
+
+#ifdef LOADERS_QUANTIZED_K
+fn load_k_u16_at(byte_offset: u32) -> u32 {
+    let word = K[byte_offset / 4u];
+    let shift = (byte_offset & 2u) * 8u;
+    return (word >> shift) & 0xFFFFu;
+}
+
+fn load_k_u32_at(byte_offset: u32) -> u32 {
+    let word_idx = byte_offset / 4u;
+    let shift = (byte_offset & 3u) * 8u;
+    let lo = K[word_idx];
+    if (shift == 0u) {
+        return lo;
+    }
+    let hi = K[word_idx + 1u];
+    return (lo >> shift) | (hi << (32u - shift));
+}
+#endif // LOADERS_QUANTIZED_K
+
+#ifdef LOADERS_QUANTIZED_V
+fn load_v_u16_at(byte_offset: u32) -> u32 {
+    let word = V[byte_offset / 4u];
+    let shift = (byte_offset & 2u) * 8u;
+    return (word >> shift) & 0xFFFFu;
+}
+
+fn load_v_u32_at(byte_offset: u32) -> u32 {
+    let word_idx = byte_offset / 4u;
+    let shift = (byte_offset & 3u) * 8u;
+    let lo = V[word_idx];
+    if (shift == 0u) {
+        return lo;
+    }
+    let hi = V[word_idx + 1u];
+    return (lo >> shift) | (hi << (32u - shift));
+}
+#endif // LOADERS_QUANTIZED_V
+
+#endif // U32_DEQUANT_HELPERS



@@ -138,7 +138,7 @@ const FLOAT_MIN: f32 = -1.0e9;
 // The number of Q rows processed per workgroup
 var<workgroup> q_shmem: array<f16, Q_TILE * HEAD_DIM_QK>;

-#ifndef KV_DIRECT
+#if !defined(K_DIRECT) || !defined(V_DIRECT)
 const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
 // we can reuse the same shmem for K and V since we only need one at a time
 var<workgroup> kv_shmem: array<f16, kv_shmem_size>;
@@ -183,13 +183,12 @@ fn load_kx4(buf: ptr<storage, array<vec4<K_TYPE>>, read_write>, scalar_index: u3
    return (*buf)[scalar_index >> 2u];
 }

-#ifndef KV_DIRECT
+#if !defined(K_DIRECT) || !defined(V_DIRECT)
 #define QUANT_SHMEM kv_shmem
 #define QUANT_OUT_TYPE f16
-#include "quant_inner_loops.tmpl"
 #include "flash_attn_quant_staging.tmpl"

-#if !defined(K_Q4_0) && !defined(K_Q8_0)
+#if !defined(K_DIRECT) && !defined(K_Q4_0) && !defined(K_Q8_0)
 fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) {
    for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE) {
        let k_row = elem_idx / HEAD_DIM_QK;
@@ -204,7 +203,7 @@ fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u
 }
 #endif

-#if !defined(V_Q4_0) && !defined(V_Q8_0)
+#if !defined(V_DIRECT) && !defined(V_Q4_0) && !defined(V_Q8_0)
 fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) {
    for (var elem_idx = local_x; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE) {
        let v_row = elem_idx / HEAD_DIM_V;
@@ -296,7 +295,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
        }

      // load k tile into shared memory
-#ifndef KV_DIRECT
+#ifndef K_DIRECT
      load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
 #endif

@@ -306,7 +305,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
      // TODO: this loop seems to be the current largest bottleneck
      // this bracket exists to scope the lifetime of variables, reducing register pressure
      {
-#ifdef KV_DIRECT
+#ifdef K_DIRECT
          let k_block_row = kv_tile + subgroup_id * SG_MAT_N;
          var k_global_offset = k_head_offset + k_block_row * params.stride_k1;
 #else
@@ -318,7 +317,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,

              var q_cur = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, 0u, false, HEAD_DIM_QK);

-#ifdef KV_DIRECT
+#ifdef K_DIRECT
              var k_cur = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + 0u, true, params.stride_k1);
 #else
              var k_cur = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + 0u, true, HEAD_DIM_QK);
@@ -328,7 +327,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
              for (; t + 1u < HEAD_DIM_QK / SG_MAT_K; t += 2u) {
                  let h0 = t * SG_MAT_K;
                  var q0 = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, h0, false, HEAD_DIM_QK);
-#ifdef KV_DIRECT
+#ifdef K_DIRECT
                  var k0 = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + h0, true, params.stride_k1);
 #else
                  var k0 = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + h0, true, HEAD_DIM_QK);
@@ -339,7 +338,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,

                  let h1 = (t + 1u) * SG_MAT_K;
                  var q1g = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, h1, false, HEAD_DIM_QK);
-#ifdef KV_DIRECT
+#ifdef K_DIRECT
                  var k1g = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + h1, true, params.stride_k1);
 #else
                  var k1g = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + h1, true, HEAD_DIM_QK);
@@ -353,7 +352,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
              if (t < HEAD_DIM_QK / SG_MAT_K) {
                  let h = t * SG_MAT_K;
                  var qn = subgroupMatrixLoad<subgroup_matrix_left<f16, SG_MAT_K, SG_MAT_M>>(&q_shmem, h, false, HEAD_DIM_QK);
-#ifdef KV_DIRECT
+#ifdef K_DIRECT
                  var kn = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&K, k_global_offset + h, true, params.stride_k1);
 #else
                  var kn = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(&kv_shmem, k_block_offset + h, true, HEAD_DIM_QK);
@@ -365,7 +364,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,

              acc = subgroupMatrixMultiplyAccumulate(q_cur, k_cur, acc);

-#ifdef KV_DIRECT
+#ifdef K_DIRECT
              k_global_offset += num_subgroups * SG_MAT_N * params.stride_k1;
 #else
              k_block_offset += num_subgroups * SG_MAT_N * HEAD_DIM_QK;
@@ -436,7 +435,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
      }

      // load v tile into shared memory
-#ifndef KV_DIRECT
+#ifndef V_DIRECT
      load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
 #endif

@@ -464,7 +463,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                  );

                  // load V submatrix from global or shared memory
-#ifdef KV_DIRECT
+#ifdef V_DIRECT
                  let v_block_row = kv_tile + kv_block * SG_MAT_N;
                  let v_global_offset = v_head_offset + v_block_row * params.stride_v1 + head_dim_block;
                  var v_sg_mat: subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K> = subgroupMatrixLoad<subgroup_matrix_right<f16, SG_MAT_N, SG_MAT_K>>(
@@ -1,3 +1,5 @@
+#include "quant_inner_loops.tmpl"
+
 #define BLOCK_SIZE 32
 #define BLOCKS_K ((HEAD_DIM_QK + BLOCK_SIZE - 1) / BLOCK_SIZE)
 #define BLOCKS_V ((HEAD_DIM_V + BLOCK_SIZE - 1) / BLOCK_SIZE)
@@ -26,49 +28,6 @@
 #define V_BYTES_PER_INNER_LOOP 4u
 #endif

-#if defined(K_Q4_0) || defined(K_Q8_0)
-fn load_k_u16_at(byte_offset: u32) -> u32 {
-    let word = K[byte_offset / 4u];
-    let shift = (byte_offset & 2u) * 8u;
-    return (word >> shift) & 0xFFFFu;
-}
-
-fn load_k_u32_at(byte_offset: u32) -> u32 {
-    let word_idx = byte_offset / 4u;
-    let shift = (byte_offset & 3u) * 8u;
-    let lo = K[word_idx];
-    if (shift == 0u) {
-        return lo;
-    }
-    let hi = K[word_idx + 1u];
-    return (lo >> shift) | (hi << (32u - shift));
-}
-#endif
-
-#if defined(V_Q4_0) || defined(V_Q8_0)
-fn load_v_u16_at(byte_offset: u32) -> u32 {
-    let word = V[byte_offset / 4u];
-    let shift = (byte_offset & 2u) * 8u;
-    return (word >> shift) & 0xFFFFu;
-}
-
-fn load_v_u32_at(byte_offset: u32) -> u32 {
-    let word_idx = byte_offset / 4u;
-    let shift = (byte_offset & 3u) * 8u;
-    let lo = V[word_idx];
-    if (shift == 0u) {
-        return lo;
-    }
-    let hi = V[word_idx + 1u];
-    return (lo >> shift) | (hi << (32u - shift));
-}
-#endif
-
-fn f16_from_u16(bits: u32) -> f16 {
-    let packed = unpack2x16float(bits);
-    return f16(packed[0]);
-}
-
 #if defined(K_Q4_0) || defined(K_Q8_0)
 fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) {
    for (var elem_idx = local_x * K_NQ; elem_idx < kv_count * HEAD_DIM_QK; elem_idx += WG_SIZE * K_NQ) {
@@ -153,7 +153,6 @@ var<workgroup> p_shmem: array<f16, Q_TILE * KV_TILE>;

 #define QUANT_SHMEM kv_shmem
 #define QUANT_OUT_TYPE f16
-#include "quant_inner_loops.tmpl"
 #include "flash_attn_quant_staging.tmpl"

 #if !defined(K_Q4_0) && !defined(K_Q8_0)
@@ -270,7 +269,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
            local_scores[slot] = FLOAT_MIN;
        }

-#ifndef KV_DIRECT
+        // The tile path stages K/V in shared memory so each tile can be reused across
+        // Q_TILE query rows. It therefore does not use the direct path.
+#ifndef K_DIRECT
        load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
 #endif

@@ -333,7 +334,9 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,

        workgroupBarrier();

-#ifndef KV_DIRECT
+        // The tile path stages K/V in shared memory so each tile can be reused across
+        // Q_TILE query rows. It therefore does not use the direct path.
+#ifndef V_DIRECT
        load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
 #endif

@@ -196,49 +196,35 @@ struct Params {

 // Just a very small float value.
 const FLOAT_MIN: f32 = -1.0e9;
+const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);

 var<workgroup> q_shmem: array<f32, HEAD_DIM_QK>;
-
-#ifndef KV_DIRECT
-const kv_shmem_size = KV_TILE * max(HEAD_DIM_QK, HEAD_DIM_V);
-// we can reuse the same shmem for K and V since we only need one at a time
-var<workgroup> kv_shmem: array<f32, kv_shmem_size>;
-#endif
-
 var<workgroup> o_shmem: array<f32, HEAD_DIM_V>;
+// note that we reuse the same storage for both since we only need one at a time
+var<workgroup> inter_shmem: array<f32, KV_TILE>;

 #ifdef MASK
 // storage for mask values
 var<workgroup> mask_shmem: array<f32, KV_TILE>;
 #endif

-// note that we reuse the same storage for both since we only need one at a time
-var<workgroup> inter_shmem: array<f32, KV_TILE>;
-
-// Storage for row max and exp sum during online softmax
-fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 {
-    var v = select(FLOAT_MIN,
-                   inter_shmem[kv_idx] * params.scale,
-                   kv_idx < KV_TILE);
-#ifdef LOGIT_SOFTCAP
-    v = params.logit_softcap * tanh(v);
+#if defined(K_DIRECT) || defined(V_DIRECT)
+// Shared memory for scale factor (d) in quantized K/V. Multiple threads use the same value,
+// so caching it is more efficient, even on the direct path.
+var<workgroup> d_shmem: array<f32, kv_shmem_size / 32>;
 #endif
-#ifdef MASK
-    if (apply_mask) {
-        var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE);
-        v += select(mask_val, slope * mask_val, has_bias);
-    }
-#endif
-    return v;
-}

-#ifndef KV_DIRECT
+// K/V shared memory handling
+#if !defined(K_DIRECT) || !defined(V_DIRECT)
+
+// we can reuse the same shmem for K and V since we only need one at a time
+var<workgroup> kv_shmem: array<f32, kv_shmem_size>;
+
 #define QUANT_SHMEM kv_shmem
 #define QUANT_OUT_TYPE f32
-#include "quant_inner_loops.tmpl"
 #include "flash_attn_quant_staging.tmpl"

-#if !defined(K_Q4_0) && !defined(K_Q8_0)
+#if !defined(K_DIRECT) && !defined(K_Q4_0) && !defined(K_Q8_0)
 fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u32) {
    for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_QK; elem_idx += WG_SIZE * 4u) {
        let k_row = elem_idx / HEAD_DIM_QK;
@@ -256,7 +242,7 @@ fn load_k_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, k_head_offset: u
 }
 #endif

-#if !defined(V_Q4_0) && !defined(V_Q8_0)
+#if !defined(V_DIRECT) && !defined(V_Q4_0) && !defined(V_Q8_0)
 fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u32) {
    for (var elem_idx = local_x * 4u; elem_idx < KV_TILE * HEAD_DIM_V; elem_idx += WG_SIZE * 4u) {
        let v_row = elem_idx / HEAD_DIM_V;
@@ -273,7 +259,24 @@ fn load_v_tile_block(local_x: u32, kv_count: u32, kv_tile: u32, v_head_offset: u
    }
 }
 #endif
+#endif // !defined(K_DIRECT) || !defined(V_DIRECT)
+
+// Storage for row max and exp sum during online softmax
+fn calc_softmax_term(kv_idx: u32, slope: f32, has_bias: bool, apply_mask: bool) -> f32 {
+    var v = select(FLOAT_MIN,
+                   inter_shmem[kv_idx] * params.scale,
+                   kv_idx < KV_TILE);
+#ifdef LOGIT_SOFTCAP
+    v = params.logit_softcap * tanh(v);
 #endif
+#ifdef MASK
+    if (apply_mask) {
+        var mask_val = select(0.0, mask_shmem[kv_idx], kv_idx < KV_TILE);
+        v += select(mask_val, slope * mask_val, has_bias);
+    }
+#endif
+    return v;
+}

@compute @workgroup_size(WG_SIZE)
 fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
@@ -355,12 +358,31 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
            inter_shmem[elem_idx] = 0.0;
        }

-      // load k tile into shared memory
-#ifndef KV_DIRECT
-      load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
+#ifdef K_DIRECT
+      // load only the scale factor (d) from each quantized block into shared memory on the direct path.
+#if defined(K_Q8_0)
+        for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_QK; j += WG_SIZE * 32) {
+            let kv_row = kv_tile + j / HEAD_DIM_QK;
+            let block_idx = (j % HEAD_DIM_QK) / 32;
+            let block_byte_base = 34 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
+            let d = f32(f16_from_u16(load_k_u16_at(block_byte_base)));
+            d_shmem[j / 32] = d;
+        }
+#elif defined(K_Q4_0)
+        for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_QK; j += WG_SIZE * 32) {
+            let kv_row = kv_tile + j / HEAD_DIM_QK;
+            let block_idx = (j % HEAD_DIM_QK) / 32;
+            let block_byte_base = 18 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
+            let d = f32(f16_from_u16(load_k_u16_at(block_byte_base)));
+            d_shmem[j / 32] = d;
+        }
 #endif
+#else
+      // load k tile into shared memory
+      load_k_tile_block(local_id.x, kv_count, kv_tile, k_head_offset);
+#endif // defined(K_DIRECT)

-      workgroupBarrier();
+        workgroupBarrier();

      // accumulate q block * k block into registers across the entire KV tile
      if (!skip_tile) {
@@ -381,9 +403,40 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                            q_shmem[q_off + 1u],
                            q_shmem[q_off + 2u],
                            q_shmem[q_off + 3u]);
-#ifdef KV_DIRECT
+#ifdef K_DIRECT
+#if defined(K_Q8_0)
+                        let kv_row = kv_tile + kv_idx;
+                        let block_idx = (i * 4u) / 32;
+                        let id_in_block = (i * 4u) % 32;
+                        let block_byte_base = 34 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
+                        let q_byte_base = block_byte_base + 2u;
+                        let d = d_shmem[(kv_idx * HEAD_DIM_QK) / 32 + block_idx];
+                        let q8u4 = load_k_u32_at(q_byte_base + id_in_block);
+                        let kv = vec4<f32>(
+                            d * f32(get_byte_i32(q8u4, 0)),
+                            d * f32(get_byte_i32(q8u4, 1)),
+                            d * f32(get_byte_i32(q8u4, 2)),
+                            d * f32(get_byte_i32(q8u4, 3)),
+                        );
+#elif defined(K_Q4_0)
+                        let kv_row = kv_tile + kv_idx;
+                        let block_idx = (i * 4u) / 32;
+                        let id_in_block = (i * 4u) % 32;
+                        let phase = id_in_block / 16;
+                        let block_byte_base = 18 * (k_head_offset + kv_row * params.stride_k1 + block_idx);
+                        let q_byte_base = block_byte_base + 2u;
+                        let d = d_shmem[(kv_idx * HEAD_DIM_QK) / 32 + block_idx];
+                        let q8u4 = load_k_u32_at(q_byte_base + (id_in_block - phase * 16u));
+                        let kv = vec4<f32>(
+                            d * (f32((get_byte(q8u4, 0) >> (phase * 4u)) & 0xFu) - 8.0),
+                            d * (f32((get_byte(q8u4, 1) >> (phase * 4u)) & 0xFu) - 8.0),
+                            d * (f32((get_byte(q8u4, 2) >> (phase * 4u)) & 0xFu) - 8.0),
+                            d * (f32((get_byte(q8u4, 3) >> (phase * 4u)) & 0xFu) - 8.0),
+                        );
+#else
                        let idx = k_head_offset + (kv_tile + kv_idx) * params.stride_k1 + (i * 4u);
                        let kv = vec4<f32>(K[idx >> 2u]);
+#endif
 #else
                        let idx = kv_idx * HEAD_DIM_QK + (i * 4u);
                        let kv = vec4<f32>(
@@ -391,7 +444,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                            kv_shmem[idx + 1u],
                            kv_shmem[idx + 2u],
                            kv_shmem[idx + 3u]);
-#endif
+#endif // defined(K_DIRECT)
                        partial_sum += dot(qv, kv);
                    }
                  }
@@ -473,12 +526,32 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
          }
      }

-      // load v tile into shared memory
-#ifndef KV_DIRECT
-      load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
-#endif

-      workgroupBarrier();
+#ifdef V_DIRECT
+      // load only `d` of quantized block into shared memory in the direct path
+#if defined(V_Q8_0)
+        for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_V; j += WG_SIZE * 32) {
+            let v_row = kv_tile + j / HEAD_DIM_V;
+            let block_idx = (j % HEAD_DIM_V) / 32;
+            let block_byte_base = 34 * (v_head_offset + v_row * params.stride_v1 + block_idx);
+            let d = f32(f16_from_u16(load_v_u16_at(block_byte_base)));
+            d_shmem[j / 32] = d;
+        }
+#elif defined(V_Q4_0)
+        for (var j = local_id.x * 32; j < KV_TILE * HEAD_DIM_V; j += WG_SIZE * 32) {
+            let v_row = kv_tile + j / HEAD_DIM_V;
+            let block_idx = (j % HEAD_DIM_V) / 32;
+            let block_byte_base = 18 * (v_head_offset + v_row * params.stride_v1 + block_idx);
+            let d = f32(f16_from_u16(load_v_u16_at(block_byte_base)));
+            d_shmem[j / 32] = d;
+        }
+#endif
+#else
+      // load v tile into shared memory
+      load_v_tile_block(local_id.x, kv_count, kv_tile, v_head_offset);
+#endif // V_DIRECT
+
+        workgroupBarrier();

      if (!skip_tile) {
          // we have P (KV_TILE) in inter_shmem and V (KV_TILE x head_dim_v) in kv_shmem
@@ -501,9 +574,38 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                      }

                      let p = inter_shmem[kv_idx];
-#ifdef KV_DIRECT
+#ifdef V_DIRECT
+#if defined(V_Q8_0)
+                        let block_idx = (vec_col * 4u) / 32;
+                        let id_in_block = (vec_col * 4u) % 32;
+                        let block_byte_base = 34 * (v_head_offset + v_row * params.stride_v1 + block_idx);
+                        let q_byte_base = block_byte_base + 2u;
+                        let d = d_shmem[(kv_idx * HEAD_DIM_V) / 32 + block_idx];
+                        let q8u4 = load_v_u32_at(q_byte_base + id_in_block);
+                        let v4 = vec4<f32>(
+                            d * f32(get_byte_i32(q8u4, 0)),
+                            d * f32(get_byte_i32(q8u4, 1)),
+                            d * f32(get_byte_i32(q8u4, 2)),
+                            d * f32(get_byte_i32(q8u4, 3)),
+                        );
+#elif defined(V_Q4_0)
+                        let block_idx = (vec_col * 4u) / 32;
+                        let id_in_block = (vec_col * 4u) % 32;
+                        let phase = id_in_block / 16;
+                        let block_byte_base = 18 * (v_head_offset + v_row * params.stride_v1 + block_idx);
+                        let q_byte_base = block_byte_base + 2u;
+                        let d = d_shmem[(kv_idx * HEAD_DIM_V) / 32 + block_idx];
+                        let q8u4 = load_v_u32_at(q_byte_base + (id_in_block - phase * 16u));
+                        let v4 = vec4<f32>(
+                            d * (f32((get_byte(q8u4, 0) >> (phase * 4u)) & 0xFu) - 8.0),
+                            d * (f32((get_byte(q8u4, 1) >> (phase * 4u)) & 0xFu) - 8.0),
+                            d * (f32((get_byte(q8u4, 2) >> (phase * 4u)) & 0xFu) - 8.0),
+                            d * (f32((get_byte(q8u4, 3) >> (phase * 4u)) & 0xFu) - 8.0),
+                        );
+#else
                      let v_idx = v_head_offset + v_row * params.stride_v1 + vec_col * 4u;
                      let v4 = vec4<f32>(V[v_idx >> 2u]);
+#endif
 #else
                      let v_idx = kv_idx * HEAD_DIM_V + vec_col * 4u;
                      let v4 = vec4<f32>(
@@ -511,7 +613,7 @@ fn main(@builtin(workgroup_id) wg_id: vec3<u32>,
                          kv_shmem[v_idx + 1u],
                          kv_shmem[v_idx + 2u],
                          kv_shmem[v_idx + 3u]);
-#endif
+#endif // defined(V_DIRECT)
                      lo += p * v4;
                  }

@@ -28,7 +28,7 @@ if (NOT ZENDNN_ROOT OR ZENDNN_ROOT STREQUAL "" OR ZENDNN_ROOT STREQUAL "OFF")
    ExternalProject_Add(
        zendnn
        GIT_REPOSITORY https://github.com/amd/ZenDNN.git
-        GIT_TAG 253b94ce0d7e9284c265fefb485714944caff9d3    # ZenDNN-2026-WW19
+        GIT_TAG 1f399a75cc0993778374a51bea49b64a57879595    # ZenDNN-2026-WW28
        PREFIX      ${ZENDNN_PREFIX}
        SOURCE_DIR  ${ZENDNN_SOURCE_DIR}
        BINARY_DIR  ${ZENDNN_BUILD_DIR}
@@ -30,6 +30,29 @@ zendnnl::common::data_type_t ggml_to_zendnn_type() {
    }
 }

+/**
+ * Builds the matmul_params shared by ggml_zendnn_matmul() and ggml_zendnn_group_matmul():
+ * dtype selection plus, for Q8_0 weights, dynamic-quant setup. Callers still need to set
+ * quant_params.src_scale.dims themselves, since that depends on the batch size(s) in use.
+ */
+template <typename TA, typename TB, typename TC>
+static zendnnl::lowoha::matmul::matmul_params ggml_zendnn_make_matmul_params(ggml_backend_zendnn_context * ctx) {
+    zendnnl::lowoha::matmul::matmul_params params;
+    params.dtypes.src = ggml_to_zendnn_type<TB>();
+    params.dtypes.wei = ggml_to_zendnn_type<TA>();
+    params.dtypes.dst = ggml_to_zendnn_type<TC>();
+    params.num_threads = ctx->n_threads;
+
+    if constexpr (std::is_same_v<TA, block_q8_0>) {
+        params.dtypes.compute = zendnnl::common::data_type_t::s8;
+        params.dynamic_quant = true;
+        params.quant_params.src_scale.buff = nullptr;
+        params.quant_params.src_scale.dt   = zendnnl::common::data_type_t::bf16;
+        params.packing.pack_format_b = 1;
+    }
+    return params;
+}
+
 /**
 * ZenDNN matmul: computes C = B * A.
 *
@@ -47,22 +70,12 @@ static bool ggml_zendnn_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int
                               const TA * A, int64_t lda, const TB * B, int64_t ldb, TC * C,
                               int64_t ldc) {

-    zendnnl::lowoha::matmul::matmul_params params;
-    params.dtypes.src = ggml_to_zendnn_type<TB>();
-    params.dtypes.wei = ggml_to_zendnn_type<TA>();
-    params.dtypes.dst = ggml_to_zendnn_type<TC>();
-    params.num_threads = ctx->n_threads;
+    zendnnl::lowoha::matmul::matmul_params params = ggml_zendnn_make_matmul_params<TA, TB, TC>(ctx);

    zendnnl::lowoha::matmul::matmul_batch_params_t batch_params;

    if constexpr (std::is_same_v<TA, block_q8_0>) {
-        params.dtypes.compute = zendnnl::common::data_type_t::s8;
-        const int64_t num_groups = k / QK8_0;
-        params.dynamic_quant = true;
-        params.quant_params.src_scale.buff = nullptr;
-        params.quant_params.src_scale.dt   = zendnnl::common::data_type_t::bf16;
-        params.quant_params.src_scale.dims = {n, num_groups};
-        params.packing.pack_format_b = 1;
+        params.quant_params.src_scale.dims = {n, k / QK8_0};
    }

    zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::matmul_direct(
@@ -223,6 +236,99 @@ struct mmid_row_mapping {
    int32_t i2;
 };

+/**
+ * ZenDNN batched matmul: computes C[i] = B[i] * A[i] for every active expert i via a single
+ * group_matmul_direct() call. Batched analogue of ggml_zendnn_matmul() - see its docs for the
+ * per-expert A/B/C shape convention. m and k are shared by every expert; n (batch size) varies
+ * per expert, hence the vector.
+ */
+template <typename TA, typename TB, typename TC>
+static bool ggml_zendnn_group_matmul(ggml_backend_zendnn_context * ctx, int64_t m, int64_t k,
+                                     const std::vector<int64_t> & n,
+                                     const std::vector<const void *> & A, int64_t lda,
+                                     const std::vector<const void *> & B, int64_t ldb,
+                                     const std::vector<void *> & C, int64_t ldc) {
+
+    const int n_experts = n.size();
+
+    zendnnl::lowoha::matmul::matmul_params base_params = ggml_zendnn_make_matmul_params<TA, TB, TC>(ctx);
+
+    std::vector<char>         layout(n_experts, 'r');
+    std::vector<bool>         trans_a(n_experts, false);
+    std::vector<bool>         trans_b(n_experts, true);
+    std::vector<int>          batch_m(n_experts);
+    std::vector<int>          batch_n(n_experts, m);
+    std::vector<int>          batch_k(n_experts, k);
+    std::vector<float>        alpha(n_experts, 1.0f);
+    std::vector<float>        beta(n_experts, 0.0f);
+    std::vector<const void *> bias(n_experts, nullptr);
+    std::vector<int>          lda_v(n_experts, lda);
+    std::vector<int>          ldb_v(n_experts, ldb);
+    std::vector<int>          ldc_v(n_experts, ldc);
+    std::vector<bool>         is_wei_const(n_experts, true);
+    std::vector<zendnnl::lowoha::matmul::matmul_params> params(n_experts, base_params);
+
+    for (int i = 0; i < n_experts; i++) {
+        batch_m[i] = n[i];
+
+        // src_scale.dims depends on this expert's row count, unlike the rest of base_params
+        if constexpr (std::is_same_v<TA, block_q8_0>) {
+            params[i].quant_params.src_scale.dims = {n[i], k / QK8_0};
+        }
+    }
+
+    zendnnl::error_handling::status_t status = zendnnl::lowoha::matmul::group_matmul_direct(
+        layout, trans_a, trans_b, batch_m, batch_n, batch_k, alpha,
+        B, ldb_v, A, lda_v, bias, beta,
+        C, ldc_v, is_wei_const, params);
+
+    if (status != zendnnl::error_handling::status_t::success) {
+        GGML_LOG_ERROR("%s, ZenDNN group matmul failed: status=%d\n", __func__, static_cast<int>(status));
+        return false;
+    }
+    return true;
+}
+
+static bool ggml_zendnn_group_gemm(ggml_backend_zendnn_context * ctx, int64_t m, int64_t k,
+                                    const std::vector<int64_t> & n,
+                                    const std::vector<const void *> & A, int64_t lda,
+                                    const std::vector<const void *> & B, int64_t ldb,
+                                    const std::vector<void *> & C, int64_t ldc,
+                                    int Atype, int Btype, int Ctype) {
+
+    assert(m >= 0);
+    for (size_t i = 0; i < n.size(); i++) {
+        assert(n[i] >= 0);
+    }
+    assert(k >= 0);
+    assert(lda >= k);
+    assert(ldb >= k);
+    assert(ldc >= m);
+
+    // categorize types
+    switch (Atype) {
+        case GGML_TYPE_F32:
+            if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32)
+                return false;
+            return ggml_zendnn_group_matmul<float, float, float>(ctx, m, k, n, A, lda, B, ldb, C, ldc);
+        case GGML_TYPE_BF16:
+            if (Btype != GGML_TYPE_BF16)
+                return false;
+            if (Ctype == GGML_TYPE_BF16)
+                return ggml_zendnn_group_matmul<ggml_bf16_t, ggml_bf16_t, ggml_bf16_t>(
+                    ctx, m, k, n, A, lda, B, ldb, C, ldc);
+            if (Ctype == GGML_TYPE_F32)
+                return ggml_zendnn_group_matmul<ggml_bf16_t, ggml_bf16_t, float>(ctx, m, k, n, A, lda, B, ldb, C, ldc);
+            return false;
+        case GGML_TYPE_Q8_0:
+            if (Btype != GGML_TYPE_F32 || Ctype != GGML_TYPE_F32)
+                return false;
+            return ggml_zendnn_group_matmul<block_q8_0, float, float>(ctx, m, k, n, A, lda, B, ldb, C, ldc);
+        default:
+            return false; // unsupported type
+    }
+}
+
 static void ggml_zendnn_compute_forward_mul_mat_id(
    ggml_backend_zendnn_context * ctx,
    ggml_tensor * dst) {
@@ -262,7 +368,8 @@ static void ggml_zendnn_compute_forward_mul_mat_id(
    std::vector<int64_t> matrix_row_counts(n_as, 0);
    std::vector<std::vector<mmid_row_mapping>> matrix_rows(n_as);

-    int64_t max_rows = 0;
+    int64_t total_rows = 0;
+    int     n_active_experts = 0;
    // group rows by expert (preprocessing step)
    for (int64_t iid1 = 0; iid1 < ids->ne[1]; ++iid1) {
        for (int id = 0; id < n_ids; ++id) {
@@ -270,66 +377,74 @@ static void ggml_zendnn_compute_forward_mul_mat_id(

            GGML_ASSERT(i02 >= 0 && i02 < n_as);

+            if (matrix_row_counts[i02] == 0) {
+                n_active_experts++;
+            }
            matrix_rows[i02].push_back({id, iid1});
            matrix_row_counts[i02]++;
-            if (matrix_row_counts[i02] > max_rows) {
-                max_rows = matrix_row_counts[i02];
-            }
+            total_rows++;
        }
    }

-    if (max_rows == 0) {
+    if (total_rows == 0) {
        return; // no rows to process
    }

    const size_t row_size = ggml_row_size(vec_dot_type, ne10);

-    // size for converting src1 rows to vec_dot_type if needed
-    const size_t nbw1 = row_size;
-    const size_t nbw2 = nbw1 * ne11;
-    const size_t nbw3 = nbw2 * ne12;
-    const size_t src1_conv_size = (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) ? ne13 * nbw3 : 0;
-
    // For Q8_0, src1 is always F32; the gather buffer must hold F32 rows (ne10*4 bytes),
    // not Q8_0-encoded rows (row_size ≈ ne10/32*34 bytes) — they differ by ~4x.
    const size_t f32_row_size = (size_t)ne10 * sizeof(float);
    const size_t gather_row_size = (src0->type == GGML_TYPE_Q8_0) ? f32_row_size : row_size;

+    if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) {
+        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    }
+
    // size for MoE gather/scatter buffers
-    const size_t wdata_cur_size = max_rows * gather_row_size;
-    const size_t dst_cur_size = max_rows * ggml_row_size(dst->type, ne01);
+    const size_t wdata_cur_size = total_rows * gather_row_size;
+    const size_t dst_cur_size = total_rows * ggml_row_size(dst->type, ne01);

    // allocate single buffer for all needs
-    const size_t total_size = src1_conv_size + wdata_cur_size + dst_cur_size;
+    const size_t total_size = wdata_cur_size + dst_cur_size;
    if (ctx->work_size < total_size) {
        ctx->work_data.reset(new char[total_size]);
        ctx->work_size = total_size;
    }

    // partition the buffer
-    char * work_data = ctx->work_data.get();
-    char * wdata_cur = work_data + src1_conv_size;
+    char * wdata_cur = ctx->work_data.get();
    char * dst_cur = wdata_cur + wdata_cur_size;

-    // ZenDNN requires FP32 for dynamic quantization, so conversion is skipped
-    if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) {
-        GGML_ASSERT(src1->type == GGML_TYPE_F32);
+    // per-expert data collected during gather, handed to ggml_zendnn_group_gemm() as one batch
+    std::vector<int64_t>      expert_row_count(n_active_experts);
+    std::vector<const void *> batch_src(n_active_experts);
+    std::vector<const void *> batch_wei(n_active_experts);
+    std::vector<void *>       batch_dst(n_active_experts);

-        #pragma omp parallel for collapse(3) num_threads(ctx->n_threads) schedule(static)
-        for (int64_t i13 = 0; i13 < ne13; ++i13) {
-            for (int64_t i12 = 0; i12 < ne12; ++i12) {
-                for (int64_t i11 = 0; i11 < ne11; ++i11) {
-                    const float * src1_f32 = (float *)((char *)src1->data + i11*nb11 + i12*nb12 + i13*nb13);
-                    void * src1_conv = (char *)work_data + i11*nbw1 + i12*nbw2 + i13*nbw3;
-                    from_float(src1_f32, src1_conv, ne10);
-                }
+    // precompute per-expert buffer offsets and batch indices for the parallel loop below
+    std::vector<int64_t> expert_wdata_off(n_as, 0);
+    std::vector<int64_t> expert_dst_off(n_as, 0);
+    std::vector<int>     expert_batch_idx(n_as, -1);
+    {
+        int64_t w_off = 0;
+        int64_t d_off = 0;
+        int     batch_idx = 0;
+        for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) {
+            if (matrix_row_counts[cur_a] == 0) {
+                continue;
            }
+            expert_wdata_off[cur_a] = w_off;
+            expert_dst_off[cur_a]   = d_off;
+            expert_batch_idx[cur_a] = batch_idx;
+            w_off += matrix_row_counts[cur_a] * gather_row_size;
+            d_off += matrix_row_counts[cur_a] * ggml_row_size(dst->type, ne01);
+            batch_idx++;
        }
    }

-    const void * wdata = (src1->type == vec_dot_type || src0->type == GGML_TYPE_Q8_0) ? src1->data : work_data;
-
-    // process each expert with gather -> gemm -> scatter pattern
+    // gather + inline-convert input rows into each expert's batch slot
+    #pragma omp parallel for num_threads(ctx->n_threads) schedule(static)
    for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) {
        const int64_t cne1 = matrix_row_counts[cur_a];

@@ -337,42 +452,57 @@ static void ggml_zendnn_compute_forward_mul_mat_id(
            continue;
        }

-        const char * src0_cur = (const char *) src0->data + cur_a*nb02;
+        const int64_t w_off     = expert_wdata_off[cur_a];
+        const int64_t d_off     = expert_dst_off[cur_a];
+        const int     batch_idx = expert_batch_idx[cur_a];

-        // gather input rows for this expert
-        #pragma omp parallel for num_threads(ctx->n_threads) schedule(static)
        for (int64_t ir1 = 0; ir1 < cne1; ++ir1) {
            const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1];
-            const int64_t id = row_mapping.i1;
+            const int64_t id  = row_mapping.i1;
            const int64_t i11 = id % ne11;
            const int64_t i12 = row_mapping.i2;

-            std::memcpy(
-                wdata_cur + ir1 * gather_row_size,
-                (const char *) wdata + (i11 + i12*ne11) * gather_row_size,
-                gather_row_size
-            );
+            const char * src_row = (const char *) src1->data + i11*nb11 + i12*nb12;
+            void *       dst_row = wdata_cur + w_off + ir1 * gather_row_size;
+
+            if (src1->type != vec_dot_type && src0->type != GGML_TYPE_Q8_0) {
+                from_float((const float *) src_row, dst_row, ne10);
+            } else {
+                // no conversion: src1 already matches vec_dot_type, or src0 is Q8_0, whose
+                // ZenDNN dynamic quantization requires the row to stay in F32
+                std::memcpy(dst_row, src_row, gather_row_size);
+            }
        }

-        // batched gemm for all tokens in this expert
-        if (!ggml_zendnn_gemm(ctx,
-                              ne01,       // m
-                              cne1,       // n
-                              ne10,       // k
-                              src0_cur,
-                              ne00,       // lda
-                              wdata_cur,
-                              ne10,       // ldb
-                              dst_cur,
-                              ne01,       // ldc
-                              src0->type,
-                              src0->type == GGML_TYPE_Q8_0 ? GGML_TYPE_F32 : vec_dot_type,
-                              dst->type)) {
-            GGML_ABORT("%s: ZenDNN gemm failed\n", __func__);
+        expert_row_count[batch_idx] = cne1;
+        batch_src[batch_idx]        = wdata_cur + w_off;
+        batch_wei[batch_idx]        = (const char *) src0->data + cur_a * nb02;
+        batch_dst[batch_idx]        = dst_cur + d_off;
+    }
+
+    if (!ggml_zendnn_group_gemm(ctx,
+                                ne01,             // m
+                                ne10,             // k
+                                expert_row_count, // n (per expert)
+                                batch_wei, ne00,  // A: weights, lda
+                                batch_src, ne10,  // B: input, ldb
+                                batch_dst, ne01,  // C: output, ldc
+                                src0->type,
+                                src0->type == GGML_TYPE_Q8_0 ? GGML_TYPE_F32 : vec_dot_type,
+                                dst->type))
+        GGML_ABORT("%s: ZenDNN group gemm failed\n", __func__);
+
+    // scatter output rows to destination
+    #pragma omp parallel for num_threads(ctx->n_threads) schedule(static)
+    for (int64_t cur_a = 0; cur_a < n_as; ++cur_a) {
+        const int64_t cne1 = matrix_row_counts[cur_a];
+
+        if (cne1 == 0) {
+            continue;
        }

-        // scatter output rows to destination
-        #pragma omp parallel for num_threads(ctx->n_threads) schedule(static)
+        const int64_t d_off = expert_dst_off[cur_a];
+
        for (int64_t ir1 = 0; ir1 < cne1; ++ir1) {
            const mmid_row_mapping & row_mapping = matrix_rows[cur_a][ir1];
            const int64_t id = row_mapping.i1;
@@ -381,7 +511,7 @@ static void ggml_zendnn_compute_forward_mul_mat_id(

            std::memcpy(
                (char *) dst->data + i1*nb1 + i2*nb2,
-                dst_cur + ir1 * ggml_row_size(dst->type, ne01),
+                dst_cur + d_off + ir1 * ggml_row_size(dst->type, ne01),
                ggml_row_size(dst->type, ne01)
            );
        }
@@ -591,22 +721,26 @@ static bool ggml_backend_zendnn_device_supports_op(ggml_backend_dev_t dev, const
                if(K <= 256 || N <= 128 || M <= 96) {
                    return false;
                }
+
+                // MUL_MAT_ID's gather+matmul+scatter approach favors a moderate expert count
+                if (op->op == GGML_OP_MUL_MAT_ID) {
+                    const int64_t n_experts = weights->ne[2];
+                    const int64_t max_experts = 32;
+                    if (n_experts > max_experts) {
+                        return false;
+                    }
+
+                    // fall back once the average rows per expert (N / n_experts) is too thin
+                    // to amortize each per-expert GEMM's overhead
+                    if (N / n_experts <= 32) {
+                        return false;
+                    }
+                }
            }
            else if (ne0 < min_batch || ne1 < min_batch || ne10 < min_batch) {
                return false;
            }

-            // MUL_MAT_ID performs best with a moderate number of experts due to its
-            // gather + batched matmul + scatter approach. Future versions will leverage
-            // ZenDNN's grouped_gemm for better scalability with larger expert counts:
-            // https://github.com/amd/ZenDNN/blob/main/docs/operator/lowoha_group_gemm_operator.md
-            if (op->op == GGML_OP_MUL_MAT_ID) {
-                const int64_t n_experts = weights->ne[2];
-                const int64_t max_experts = 32;
-                if (n_experts > max_experts) {
-                    return false;
-                }
-            }
            switch (weights->type) {
                case GGML_TYPE_F32:
                case GGML_TYPE_BF16:
@@ -1 +1 @@
-9be313313c8ecb9488911bd64550190e3ed80f38
+06ca97616793248fadb410ea8d69c7511b2005e4
@@ -474,6 +474,9 @@ llama_context::llama_context(
 }

 llama_context::~llama_context() {
+    // wait for any pending asynchronous copies into the output buffers before they are freed
+    synchronize();
+
    if (!model.hparams.no_alloc) {
        for (size_t i = 0; i < backend_ptrs.size(); ++i) {
            ggml_backend_t             backend = backend_ptrs[i];
@@ -1417,13 +1420,17 @@ int llama_context::encode(const llama_batch & batch_inp) {
    // micro-batching is not possible for non-causal encoding, so we process the batch in a single shot
    GGML_ASSERT(cparams.n_ubatch >= n_tokens && "encoder requires n_ubatch >= n_tokens");

+    // TODO: this clear of the buffer can easily be forgotten - need something better
+    // sync first so any in-flight async copies into embd_seq complete before it is freed
+    if (!embd_seq.empty()) {
+        synchronize();
+    }
+    embd_seq.clear();
+
    if (t_compute_start_us == 0) {
        t_compute_start_us = ggml_time_us();
    }

-    // TODO: this clear of the buffer can easily be forgotten - need something better
-    embd_seq.clear();
-
    sched_reserve();

    n_queued_tokens += n_tokens;
@@ -1762,13 +1769,18 @@ int llama_context::decode(const llama_batch & batch_inp) {

    GGML_ASSERT((cparams.causal_attn || cparams.n_ubatch >= n_tokens_all) && "non-causal attention requires n_ubatch >= n_tokens");

+    // TODO: this clear of the buffer can easily be forgotten - need something better
+    // sync first so any in-flight async copies into embd_seq complete before it is freed
+    if (!embd_seq.empty()) {
+        synchronize();
+    }
+    embd_seq.clear();
+
    if (t_compute_start_us == 0) {
        t_compute_start_us = ggml_time_us();
    }
    n_queued_tokens += n_tokens_all;

-    // TODO: this clear of the buffer can easily be forgotten - need something better
-    embd_seq.clear();
    output_swaps.clear();

    sched_reserve();
@@ -3541,6 +3553,22 @@ llama_context * llama_init_from_model(
        }
    }

+    if ((model->hparams.is_mla() || model->arch == LLM_ARCH_DEEPSEEK4) && params.type_k != params.type_v) {
+        LLAMA_LOG_ERROR("%s: model does not support different K (%s) and V (%s) cache types\n", __func__, ggml_type_name(params.type_k), ggml_type_name(params.type_v));
+        return nullptr;
+    }
+
+    if (ggml_is_quantized(params.type_v) && params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_ENABLED) {
+        if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_AUTO) {
+            LLAMA_LOG_INFO("%s: enabling flash_attn since it is required for quantized V cache\n", __func__);
+            params.flash_attn_type = LLAMA_FLASH_ATTN_TYPE_ENABLED;
+        }
+        if (params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) {
+            LLAMA_LOG_ERROR("%s: quantized V cache requires flash_attn to be enabled\n", __func__);
+            return nullptr;
+        }
+    }
+
    if (params.flash_attn_type != LLAMA_FLASH_ATTN_TYPE_DISABLED && ggml_is_quantized(params.type_k)) {
        const uint32_t blck_size = ggml_blck_size(params.type_k);
        for (uint32_t il = 0; il < model->hparams.n_layer(); ++il) {
@@ -3563,11 +3591,6 @@ llama_context * llama_init_from_model(
        }
    }

-    if (ggml_is_quantized(params.type_v) && params.flash_attn_type == LLAMA_FLASH_ATTN_TYPE_DISABLED) {
-        LLAMA_LOG_ERROR("%s: V cache quantization requires flash_attn\n", __func__);
-        return nullptr;
-    }
-
    if (params.pooling_type != LLAMA_POOLING_TYPE_UNSPECIFIED &&
        params.pooling_type != model->hparams.pooling_type) {
        //user-specified pooling-type is different from the model default
@@ -87,7 +87,7 @@ function(llama_build_and_test source)
    set(multiValueArgs ARGS)
    cmake_parse_arguments(LLAMA_TEST "${options}" "${oneValueArgs}" "${multiValueArgs}" ${ARGN})

-    set(TEST_SOURCES ${source} ${LLAMA_TEST_UNPARSED_ARGUMENTS} get-model.cpp)
+    set(TEST_SOURCES ${source} ${LLAMA_TEST_UNPARSED_ARGUMENTS})

    if (NOT DEFINED LLAMA_TEST_LABEL)
        set(LLAMA_TEST_LABEL "main")
@@ -148,7 +148,7 @@ if (LLAMA_LLGUIDANCE)
    llama_build_and_test(test-grammar-llguidance.cpp ARGS ${PROJECT_SOURCE_DIR}/models/ggml-vocab-llama-bpe.gguf)
 endif ()

-llama_build(test-recurrent-state-rollback.cpp get-model.cpp)
+llama_build(test-recurrent-state-rollback.cpp)

 if (NOT WIN32 OR NOT BUILD_SHARED_LIBS)
    # these tests are disabled on Windows because they use internal functions not exported with LLAMA_API (when building with shared libraries)
@@ -279,8 +279,9 @@ llama_build_and_test(test-save-load-state.cpp LABEL "model" ARGS -m "${MODEL_DES
 set_tests_properties(test-save-load-state PROPERTIES FIXTURES_REQUIRED test-download-model)

 if (APPLE)
-    llama_build(test-rset-release.cpp get-model.cpp)
+    llama_build(test-rset-release.cpp)
 endif()
+
 if (NOT GGML_BACKEND_DL)
    # these tests use the backends directly and cannot be built with dynamic loading
    llama_build_and_test(test-barrier.cpp)
@@ -1,21 +0,0 @@
-#include <cstdio>
-#include <cstdlib>
-#include <cstring>
-
-#include "get-model.h"
-
-char * get_model_or_exit(int argc, char *argv[]) {
-    char * model_path;
-    if (argc > 1) {
-        model_path = argv[1];
-
-    } else {
-        model_path = getenv("LLAMACPP_TEST_MODELFILE");
-        if (!model_path || strlen(model_path) == 0) {
-            fprintf(stderr, "\033[33mWARNING: No model file provided. Skipping this test. Set LLAMACPP_TEST_MODELFILE=<gguf_model_path> to silence this warning and run this test.\n\033[0m");
-            exit(EXIT_SUCCESS);
-        }
-    }
-
-    return model_path;
-}
@@ -1,2 +0,0 @@
-#pragma once
-char * get_model_or_exit(int, char*[]);
@@ -1,15 +1,13 @@
 // ref: https://github.com/ggml-org/llama.cpp/issues/4952#issuecomment-1892864763

-#include <cstdio>
-#include <string>
 #include <thread>

 #include "llama.h"
-#include "get-model.h"
+#include "common.h"

 // This creates a new context inside a pthread and then tries to exit cleanly.
 int main(int argc, char ** argv) {
-    auto * model_path = get_model_or_exit(argc, argv);
+    auto * model_path = common_get_model_or_exit(argc, argv);

    std::thread([&model_path]() {
        llama_backend_init();
@@ -8013,6 +8013,7 @@ static const ggml_type other_types[] = {
    GGML_TYPE_Q5_0, GGML_TYPE_Q5_1,
    GGML_TYPE_Q8_0,
    GGML_TYPE_Q1_0,
+    GGML_TYPE_Q2_0,
    GGML_TYPE_Q2_K, GGML_TYPE_Q3_K,
    GGML_TYPE_Q5_K,
    GGML_TYPE_Q6_K,
@@ -8328,7 +8329,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
            test_cases.emplace_back(new test_conv_2d(
                { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
                { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
-                kernel_type, 1, 1, 0, 0, 1, 1, false));
+                kernel_type, 1, 1, 0, 0, 1, 1, false));  // bool cwhn = false
+            test_cases.emplace_back(new test_conv_2d(
+                { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
+                { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
+                kernel_type, 1, 1, 0, 0, 1, 1, true));  // bool cwhn = true
        }
    }
 #endif
@@ -8357,7 +8362,9 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
                                        calc_conv_output_size(H, KH, s1, p1, d1) > 0) {
                                        for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
                                            test_cases.emplace_back(new test_conv_2d(
-                                                { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false));
+                                                { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, false)); // bool cwhn = false
+                                            test_cases.emplace_back(new test_conv_2d(
+                                                { W, H, Cin, 2 }, { KW, KH, Cin, Cout }, kernel_type, s0, s1, p0, p1, d0, d1, true));  // bool cwhn = true
                                        }
                                    }
                                }
@@ -8369,7 +8376,8 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
        }
    }
    for (auto kernel_type : {GGML_TYPE_F32, GGML_TYPE_F16}) {
-        test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false));
+        test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, false)); // bool cwhn = false
+        test_cases.emplace_back(new test_conv_2d({ 256, 256, 192, 1 }, { 3, 3, 192, 96 }, kernel_type, 1, 1, 1, 1, 1, 1, true));  // bool cwhn = true
    }

    // sycl backend will limit task global_range < MAX_INT
@@ -8837,6 +8845,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_Q8_0, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));
    test_cases.emplace_back(new test_mul_mat(GGML_TYPE_MXFP4, GGML_TYPE_F32, 2880, 32, 2880, {1, 1}, {1, 1}));

+    // m == 1, with n on both sides of MMVF_MAX_BATCH_SIZE (8): mmvf below, operand swap above
+    for (int64_t n : {1, 7, 8, 9, 16, 128, 512}) {
+        test_cases.emplace_back(new test_mul_mat(GGML_TYPE_F32, GGML_TYPE_F32, 1, n, 2048, {1, 1}, {1, 1}));
+    }

 #if 0
    {
@@ -9563,6 +9575,10 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_eval() {
    test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_Q4_0));
    test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q1_0));
    test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q1_0, GGML_TYPE_F16));
+    test_cases.emplace_back(new test_flash_attn_ext(128, 128, 4, {1, 1}, 96, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_Q2_0));
+    test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_Q4_0));
+    test_cases.emplace_back(new test_flash_attn_ext(64, 128, 4, {1, 1}, 128, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q4_0, GGML_TYPE_Q2_0));
+    test_cases.emplace_back(new test_flash_attn_ext(128, 64, 4, {1, 1}, 64, 2, true, false, 0, 0, GGML_PREC_F32, GGML_TYPE_Q2_0, GGML_TYPE_F16));

    // large-KV F16 cases (Qwen3.6-27B geometry and a llama-class control): the upstream matrix
    // stops at kv=1024, blind to long-context FA bugs (e.g. the oneDNN SDPA ordering race on BMG).
@@ -9748,7 +9764,11 @@ static std::vector<std::unique_ptr<test_case>> make_test_cases_perf() {
            test_cases.emplace_back(new test_conv_2d(
                { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
                { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
-                kernel_type, 1, 1, 0, 0, 1, 1, false));
+                kernel_type, 1, 1, 0, 0, 1, 1, false));   // bool cwhn = false
+            test_cases.emplace_back(new test_conv_2d(
+                { act_case[iwh_idx], act_case[iwh_idx], act_case[Cin_idx], act_case[B_idx] },
+                { act_case[kwh_idx], act_case[kwh_idx], act_case[Cin_idx], act_case[Cout_idx] },
+                kernel_type, 1, 1, 0, 0, 1, 1, true));    // bool cwhn = true
        }
    }

@@ -1,7 +1,6 @@
 #include "ggml.h"
 #include "llama.h"
 #include "llama-cpp.h"
-#include "get-model.h"
 #include "common.h"

 #ifdef NDEBUG
@@ -1136,7 +1135,7 @@ int main(int argc, char ** argv) {
    test_args args = parse_cli(argc, argv);

    if (args.model.empty()) {
-        args.model = get_model_or_exit(1, argv);
+        args.model = common_get_model_or_exit(1, argv);
    }

    {
@@ -1,10 +1,10 @@
 #include "llama.h"
-#include "get-model.h"
+#include "common.h"

 #include <cstdlib>

 int main(int argc, char *argv[] ) {
-    auto * model_path = get_model_or_exit(argc, argv);
+    auto * model_path = common_get_model_or_exit(argc, argv);
    auto * file = fopen(model_path, "r");
    if (file == nullptr) {
        fprintf(stderr, "no model at '%s' found\n", model_path);
@@ -216,18 +216,18 @@ static std::string snapshot_file_from_name(const std::string & name) {
 }

 static const remote_model_spec model_specs[] = {
-    { "ggml-org/Qwen3-0.6B-GGUF",                   "Q8_0"   },
-    { "ggml-org/GLM-4.6V-GGUF",                     "Q8_0"   },
-    { "ggml-org/Step-3.5-Flash-GGUF",               "Q4_K"   },
-    { "ggml-org/Qwen3-Coder-Next-GGUF",             "Q8_0"   },
-    { "ggml-org/Qwen3-14B-GGUF",                    "Q8_0"   },
-    { "ggml-org/Nemotron-Nano-3-30B-A3B-GGUF",      "Q8_0"   },
-    { "ggml-org/gpt-oss-120b-GGUF",                 "mxfp4"  },
-    { "ggml-org/gemma-3-4b-it-GGUF",                "Q8_0"   },
-    { "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF", "Q4_K_M" },
-    { "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF",   "IQ1_M"  },
-    { "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF",      "IQ1_S"  }, // TODO: swap with ggml-org if/when it's released
-    { "bartowski/Qwen_Qwen3.5-27B-GGUF",            "Q8_0"   }, // TODO: swap with ggml-org if/when it's released
+    { "ggml-org/Qwen3-0.6B-GGUF",                     "Q8_0"   },
+    { "ggml-org/GLM-4.6V-GGUF",                       "Q8_0"   },
+    { "ggml-org/Step-3.5-Flash-GGUF",                 "Q4_K"   },
+    { "ggml-org/Qwen3-Coder-Next-GGUF",               "Q8_0"   },
+    { "ggml-org/Qwen3-14B-GGUF",                      "Q8_0"   },
+    { "ggml-org/NVIDIA-Nemotron-Nano-3-30B-A3B-GGUF", "Q8_0"   },
+    { "ggml-org/gpt-oss-120b-GGUF",                   "mxfp4"  },
+    { "ggml-org/gemma-3-4b-it-GGUF",                  "Q8_0"   },
+    { "bartowski/Meta-Llama-3.1-70B-Instruct-GGUF",   "Q4_K_M" },
+    { "bartowski/deepseek-ai_DeepSeek-V3.1-GGUF",     "IQ1_M"  },
+  //{ "bartowski/Qwen_Qwen3.5-397B-A17B-GGUF",        "IQ1_S"  }, // TODO: swap with ggml-org if/when it's released
+    { "ggml-org/Qwen3.6-27B-GGUF",                    "Q8_0"   },
 };

 static const int n_model_specs = (int) (sizeof(model_specs) / sizeof(model_specs[0]));
@@ -3,14 +3,14 @@
 // thus, this test is not run by default
 // example model to run with: google/gemma-4-E4B-it-qat-q4_0-gguf

+#include "llama.h"
+#include "common.h"
+
 #include <cstdint>
 #include <mach/mach.h>
 #include <mach/mach_host.h>
 #include <unistd.h>

-#include "llama.h"
-#include "get-model.h"
-
 static uint64_t wired_memory() {
    vm_statistics64_data_t vmstat;
    mach_msg_type_number_t count = HOST_VM_INFO64_COUNT;
@@ -21,7 +21,7 @@ static uint64_t wired_memory() {
 }

 int main(int argc, char ** argv) {
-    auto * model_path = get_model_or_exit(argc, argv);
+    auto * model_path = common_get_model_or_exit(argc, argv);

    llama_backend_init();

@@ -33,7 +33,7 @@ enum resize_algo {
    RESIZE_ALGO_BILINEAR, // stretch to target resolution
    RESIZE_ALGO_BICUBIC, // center-crop when aspect ratio doesn't match
    RESIZE_ALGO_BICUBIC_PILLOW,
-    // RESIZE_ALGO_LANCZOS, // TODO
+    RESIZE_ALGO_LANCZOS,
 };

 // Padding style for img_tool::resize
@@ -68,6 +68,9 @@ struct img_tool {
                case RESIZE_ALGO_BICUBIC_PILLOW:
                    resize_bicubic_pillow(src, dst, target_resolution.width, target_resolution.height);
                    break;
+                case RESIZE_ALGO_LANCZOS:
+                    resize_lanczos_pillow(src, dst, target_resolution.width, target_resolution.height);
+                    break;
                default:
                    throw std::runtime_error("Unsupported resize algorithm");
            }
@@ -97,6 +100,9 @@ struct img_tool {
                case RESIZE_ALGO_BICUBIC_PILLOW:
                    resize_bicubic_pillow(src, resized_image, new_width, new_height);
                    break;
+                case RESIZE_ALGO_LANCZOS:
+                    resize_lanczos_pillow(src, resized_image, new_width, new_height);
+                    break;
                default:
                    throw std::runtime_error("Unsupported resize algorithm");
            }
@@ -337,22 +343,50 @@ private:
        }
    }

-    // Bicubic resize function using Pillow's ImagingResample algorithm
+    // Pillow-compatible separable resampling (Bicubic and Lanczos)
    // Adapted from https://github.com/python-pillow/Pillow/blob/main/src/libImaging/Resample.c
    //
-    // Key Difference with resize_bicubic:
-    // 1. Uses separable filtering: horizontal pass followed by vertical pass
+    // Key properties:
+    // 1. Separable filtering: horizontal pass followed by vertical pass
    // 2. Pre-computes normalized filter coefficients for each output pixel
-    // 3. Applies convolution using fixed-point integer arithmetic for performance
+    // 3. Fixed-point integer arithmetic (22 fractional bits) for speed and determinism
    static bool resize_bicubic_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
+        return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/false);
+    }
+
+    // Lanczos-3 (support radius 3), matches Pillow's Image.LANCZOS
+    static bool resize_lanczos_pillow(const clip_image_u8 & img, clip_image_u8 & dst, int target_width, int target_height) {
+        return resize_pillow(img, dst, target_width, target_height, /*use_lanczos=*/true);
+    }
+
+    static bool resize_pillow(
+            const clip_image_u8 & img,
+            clip_image_u8 & dst,
+            int target_width,
+            int target_height,
+            bool use_lanczos) {
        // Fixed-point precision: 22 bits = 32 (int32_t) - 8 (uint8_t pixels) - 2 (headroom for accumulation)
        // This allows encoding fractional weights as integers: weight * 2^22
        const int PRECISION_BITS = 32 - 8 - 2;

-        // Bicubic filter function with a = -0.5 (Note that GGML/PyTorch takes a = -0.75)
+        // Resample filter: Lanczos-3 (support [-3, 3]) or bicubic with a = -0.5 (support [-2, 2])
+        // Note: GGML/PyTorch bicubic uses a = -0.75, Pillow uses a = -0.5
        // Returns filter weight for distance x from pixel center
-        // Support: [-2, 2], meaning the filter influences pixels within 2 units of distance
-        auto bicubic_filter = [](double x) -> double {
+        auto resample_filter = [use_lanczos](double x) -> double {
+            if (use_lanczos) {
+                if (-3.0 <= x && x < 3.0) {
+                    auto sinc = [](double v) {
+                        if (v == 0.0) {
+                            return 1.0;
+                        }
+                        const double pi_v = v * 3.141592653589793238462643383279502884;
+                        return std::sin(pi_v) / pi_v;
+                    };
+                    return sinc(x) * sinc(x / 3.0);
+                }
+                return 0.0;
+            }
+
            constexpr double a = -0.5;
            if (x < 0.0) {
                x = -x;
@@ -366,8 +400,8 @@ private:
            return 0.0;  // Zero outside [-2, 2]
        };

-        // Filter support radius: bicubic extends 2 pixels in each direction
-        constexpr double filter_support = 2.0;
+        // Filter support radius: 2 for bicubic, 3 for lanczos
+        const double filter_support = use_lanczos ? 3.0 : 2.0;

        // Clipping function for 8-bit values
        auto clip8 = [](int val) -> uint8_t {
@@ -434,7 +468,7 @@ private:
                // Compute filter weights for each contributing input pixel
                for (x = 0; x < xmax; x++) {
                    // Distance from input pixel center to output pixel center in input space
-                    double w = bicubic_filter((x + xmin - center + 0.5) * ss);
+                    double w = resample_filter((x + xmin - center + 0.5) * ss);
                    pre_weights[xx * ksize + x] = w;
                    ww += w;  // Accumulate for normalization
                }
@@ -463,6 +497,12 @@ private:
            const double fxp_scale = std::ldexp(1.0, PRECISION_BITS); // 1.0 * 2^PRECISION_BITS

            for (int i = 0; i < outSize * ksize; i++) {
+                if (use_lanczos) {
+                    // Pillow adds +/- 0.5 then truncates toward zero; std::round would round twice
+                    const double rounded = pre_weights[i] * fxp_scale + (pre_weights[i] < 0 ? -0.5 : 0.5);
+                    weights[i] = static_cast<int32_t>(rounded);
+                    continue;
+                }
                double tmp_val = pre_weights[i] * fxp_scale;
                if (pre_weights[i] < 0) {
                    tmp_val -= 0.5;
@@ -78,31 +78,41 @@ struct server_batch {
    };
    std::vector<token> tokens;
    int32_t n_tokens_alloc = 0;
+    int32_t n_embd = 0;

    // track if given slot can be batched with slots already in the batch
    server_slot * slot_batched = nullptr;

+    // in embd mode, we temporarily swap out the tokens arr and restore it on clear()
+    bool has_embd = false;
+    llama_token * tokens_ptr = nullptr;
+    std::vector<float> embd;
+
    float  alora_scale       = -1.0f;
    size_t alora_disabled_id = 0;

    server_batch() {
-        batch.token = nullptr; // sentinel: uninitialized batch
+        batch.pos = nullptr; // sentinel: uninitialized batch
    }

    ~server_batch() {
-        if (batch.token != nullptr) {
+        if (batch.pos != nullptr) {
+            clear();
            llama_batch_free(batch);
        }
    }

-    void init(int32_t n_tokens_alloc) {
+    void init(int32_t n_tokens_alloc, int32_t n_embd) {
        this->n_tokens_alloc = n_tokens_alloc;
+        this->n_embd = n_embd;
        batch = llama_batch_init(n_tokens_alloc, 0, 1);
+        tokens_ptr = batch.token;
        tokens.reserve(n_tokens_alloc);
    }

    bool add(int32_t id_slot, llama_token token, llama_pos pos, bool output) {
-        GGML_ASSERT(batch.token != nullptr);
+        GGML_ASSERT(!has_embd); // cannot mix tokens + embd in same batch
+        GGML_ASSERT(batch.pos != nullptr);
        if ((int32_t)tokens.size() >= n_tokens_alloc) {
            return false;
        }
@@ -110,13 +120,30 @@ struct server_batch {
        return true;
    }

+    bool add(int32_t id_slot, const std::vector<float> & embd_in, llama_pos pos, bool output) {
+        GGML_ASSERT(batch.pos != nullptr);
+        if ((int32_t)tokens.size() >= n_tokens_alloc) {
+            return false;
+        }
+        tokens.push_back({ id_slot, LLAMA_TOKEN_NULL, pos, output });
+        has_embd = true;
+        embd.insert(embd.end(), embd_in.begin(), embd_in.end());
+        return true;
+    }
+
    void clear() {
        tokens.clear();
+        embd.clear();
        common_batch_clear(batch);
        slot_batched      = nullptr;
        alora_scale       = -1.0f;
        alora_disabled_id = 0;
        batch_rendered    = false;
+        has_embd          = false;
+        if (batch.token == nullptr) {
+            batch.token = tokens_ptr;
+            batch.embd  = nullptr;
+        }
    }

    int32_t size() const {
@@ -129,25 +156,33 @@ struct server_batch {
    }

    void render() {
-        GGML_ASSERT(batch.token != nullptr);
+        GGML_ASSERT(!batch_rendered);
+        GGML_ASSERT(batch.pos != nullptr);
        common_batch_clear(batch);
        for (int32_t i = 0; i < size(); i++) {
            const auto & t = tokens[i];
            common_batch_add(batch, t.token, t.pos, { t.id_slot }, t.output);
        }
+        if (has_embd) {
+            batch.token = nullptr; // will be restored on clear()
+            batch.embd  = embd.data();
+        }
        batch_rendered = true;
    }

    llama_batch get_view(int32_t off, int32_t n_tokens) const {
-        GGML_ASSERT(batch.token != nullptr);
+        GGML_ASSERT(batch.pos != nullptr);
        GGML_ASSERT(batch_rendered);
        GGML_ASSERT(off >= 0 && off < size());
        GGML_ASSERT(n_tokens > 0 && off + n_tokens <= size());

+        auto * token = batch.token ? batch.token + off          : nullptr;
+        auto * embd  = batch.embd  ? batch.embd  + off * n_embd : nullptr;
+
        llama_batch view = {
            n_tokens,
-            batch.token    + off,
-            nullptr,
+            token,
+            embd,
            batch.pos      + off,
            batch.n_seq_id + off,
            batch.seq_id   + off,
@@ -270,6 +305,10 @@ struct server_slot {

    llama_token sampled; // in speculative mode, this is the last accepted token

+    // for TTS models, this is the embd generated from prev step, decode this to generate next hidden state
+    // corresponding to one token position (size = n_embd)
+    std::vector<float> inp_embd;
+
    // stats
    size_t n_sent_text = 0; // number of sent text character

@@ -378,7 +417,9 @@ struct server_slot {
    bool can_batch_with(server_slot & other_slot) const {
        GGML_ASSERT(task);

-        return task->type == other_slot.task->type && are_lora_equal(lora, other_slot.lora);
+        return task->type == other_slot.task->type
+            && inp_embd.size() == other_slot.inp_embd.size()
+            && are_lora_equal(lora, other_slot.lora);
    }

    bool has_budget(const common_params & global_params) {
@@ -444,7 +485,11 @@ struct server_slot {
            // no speculative decoding
            i_batch = batch.size();

-            add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true);
+            if (!inp_embd.empty()) {
+                add_ok &= batch.add(id, inp_embd, prompt.tokens.pos_next(), true);
+            } else {
+                add_ok &= batch.add(id, sampled, prompt.tokens.pos_next(), true);
+            }

            SLT_DBG(*this, "slot decode token, id=%d, n_ctx = %d, n_tokens = %d, truncated = %d\n",
                    sampled, n_ctx, prompt.n_tokens(), truncated);
@@ -1334,7 +1379,8 @@ private:
        // note that n_batch can be > n_ctx (e.g. for non-causal attention models such as BERT where the KV cache is not used)
        {
            const int32_t n_batch = llama_n_batch(ctx_tgt);
-            batch.init(std::max(n_batch, params_base.n_parallel));
+            const int32_t n_embd  = llama_model_n_embd_inp(model_tgt);
+            batch.init(std::max(n_batch, params_base.n_parallel), n_embd);
        }

        if (params_base.cache_ram_mib != 0) {
@@ -3578,6 +3624,15 @@ private:
            n_empty_consecutive = 0;
        }

+        // TODO @ngxson : dft model may have different n_embd than the tgt model, so we check & reject if that's the case
+        // this case is not currently used by any models, but may need to be supported in the future
+        if (spec && batch.has_embd) {
+            if (llama_model_n_embd_inp(model_dft) != llama_model_n_embd_inp(model_tgt)) {
+                SRV_ERR("%s", "unsupported batch.has_embd + spec case\n");
+                throw std::runtime_error("unsupported batch.has_embd + spec case");
+            }
+        }
+
        const int ret = llama_decode(ctx_tgt, batch_view);

        metrics.on_decoded(slots);
Author	SHA1	Message	Date
Neo ZhangandGitHub	d5d3e05bf8	[SYCL] support the missed types in cpy (#26005 ) * support the missed types in cpy * use correct funct * rm unused code	2026-07-31 10:25:16 +03:00
fairydreaming GitHub Stanisław Szymczyk	69e62fc77c	llama : enforce the same K and V cache types for DeepSeek V4; enable FA if V cache is quantized (#25871 ) * llama : enforce the same K and V cache types for DeepSeek V4; enable FA if V cache is quantized * llama : enforce the same K and V cache types for MLA models --------- Co-authored-by: Stanisław Szymczyk <sszymczy@gmail.com>	2026-07-31 10:03:30 +03:00
Sachin SharmaandGitHub	1e22599522	ggml-zendnn : group matmul direct API for mul_mat_id (#25918 ) * ggml-zendnn : group matmul API for mul_mat_id * ggml-zendnn : scale MUL_MAT_ID fallback threshold by expert count	2026-07-31 09:40:52 +03:00
Neo Zhang GitHub Neo Zhang Jianyu	1c5b89ff63	sycl : support dev2dev memcpy by DEV2DEV_MEMCPY_FORWARD (#26234 ) Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>	2026-07-31 09:20:28 +03:00
Neo ZhangandGitHub	a2be61dc87	[SYCL] Support q2 mul_mat (#26231 ) * support q2_0 in mul_mat * support more q2_0 case	2026-07-31 09:19:41 +03:00
TitaniumtownandGitHub	1553725965	sycl: fuse RMS_NORM + MUL (#26015 )	2026-07-31 09:17:53 +03:00
Masashi YoshimuraandGitHub	8f4646a63e	ggml-webgpu: improve flash_attn_vec for quantized KV at long contexts (#25956 ) * improve fa of quantized kv cache * Fix some bugs and some comments. * fix v type check and some comments * Fix build error caused by rebasing * editorconfig checking pass	2026-07-31 09:08:40 +03:00
Xuan-Son NguyenandGitHub	5f55650a78	mtmd: add lanczos resize method [no release] (#26341 )	2026-07-30 21:59:49 +02:00
Xuan-Son NguyenandGitHub	b4ca032ae3	server: support inp embd to generate next token (#26313 ) * server: support embd for sampled token * fix ~server_batch()	2026-07-30 21:40:38 +02:00
Jeff BolzandGitHub	ea63b4d32e	vulkan: Support quantized concat (#25684 )	2026-07-30 13:11:32 -05:00
pmaybankandGitHub	958d9c0b61	Test support for alternative conv layout (#25617 ) * add bool cwhn = true to conv_2d test cases * add layout check at graph building time * extend layout checks for conv2d.cu kernel * in CPU back-end kernel needs to be stored contiguously to prevent test failures with cwhn=1 * trim white space * do op support check in vulkan backend * fix CI failure and vulkan run-time assert failure by introducing new graph build-time check in ggml_backend_vk_device_supports_op * add additional check in support_op function for Vulkan to fix run-time assert failure	2026-07-31 01:14:16 +08:00
o7siandGitHub	432d7ffe2c	llama-context : sync pending async copies before clearing embd_seq (#25676 )	2026-07-30 19:48:00 +03:00
Georgi GerganovandGitHub	47f686f53f	tests : avoid building get-model.cpp many times (#26317 ) * tests : remove get-model.cpp * tests : fix quant type selection	2026-07-30 19:34:04 +03:00
Robert EsclapezandGitHub	e1a1abb787	ggml-cuda: Allow transpose-free gemmv computation (#26171 ) When matrix's weights are shaped 1xK is leverage a transpose-free computation to use mat_mul_vec_f.	2026-07-30 21:39:46 +08:00
Georgi GerganovandGitHub	6b36c23056	readme : refresh (#26280 ) * docs : center badges and links, remove Hot topics - Use <div align="center"> for GitHub-compatible centering - Add dev branches and compile times links - Remove Hot topics section Assisted-by: llama.cpp:Qwen3.6-27B * readme : remove sections * docs : center badges, remove Hot topics, extract sections, remove tools - Use <div align="center"> for GitHub-compatible centering - Add dev branches and compile times links - Add lib llama API and llama-server REST API links - Remove Hot topics section - Remove Recent API changes section - Extract XCFramework section into docs/xcframework.md - Extract Completions section into docs/completions.md - Extract Obtaining and quantizing models into docs/models.md - Remove tools usage sections (llama-cli, llama-server, etc.) - Move Contributing section to the end Assisted-by: llama.cpp:Qwen3.6-27B * cont : arrange links * cont : fix ws * cont : remove seminal papers * cont : change sample model * cont : trim-down contributing section * cont : sort backends alphabetically * cont : words * cont : add fig captions * docs : models words * readme : shorter caption * cont : fix typo * cont : add window frame to screenshot	2026-07-30 16:14:37 +03:00
Georgi Gerganov	9ebfc3a8cf	sync : ggml	2026-07-30 15:44:24 +03:00
Georgi Gerganov	6a4c3357c8	ggml : bump version to 0.18.0 (ggml/1576)	2026-07-30 15:44:24 +03:00
Pasha KhosraviandGitHub	9b2a088819	CUDA: add Q2_0 support (#25707 )	2026-07-30 12:33:25 +03:00