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Thiago PadilhaandGeorgi Gerganov
f91123d2d0
Build QSA blocks per sequence in token order and select complete blocks before expanding them to cache cells. Keep only the incomplete tail unconditionally visible and rotate pooled keys with the first token's full M-RoPE position. This prevents unified-cache sequences from sharing pooled indexer keys and avoids replacing padded tail entries with extra history tokens. Synthetic Qwen4 architecture, exact mask, F16 and Q8_0 state, sequence-copy, Metal, and AddressSanitizer checks pass. Assisted-by: Codex qwen4exp: support independent PLE embedding widths Size the PLE key and value projections from the concatenated n-gram embedding instead of assuming it matches the model hidden width. Validate the head count before narrowing it to the stored type. Add a synthetic PLE model with a 64-wide embedding and a 256-wide hidden state, then verify inference and model roundtrip. Assisted-by: Codex qwen4exp: validate model metadata Reject invalid GDN, hyper-connection, QSA, and PLE dimensions during model loading instead of aborting later while building the graph. Validate PLE array lengths before copying them into fixed storage. The released configuration and synthetic Qwen4 architecture tests pass. Assisted-by: Codex qwen4exp: update indexer cache after sequence copies Treat cached indexer keys as unrotated data and apply pending cache updates alongside the attention and recurrent state. This copies indexer data during non-unified cross-stream sequence copies without applying RoPE shifts to raw keys. Assisted-by: Codex qwen4exp: enable recurrent state rollback Assisted-by: Codex qwen4exp: disable tensor split Assisted-by: Codex metal: align dynamic threadgroup memory Assisted-by: Codex metal: widen expert matmul thread index Assisted-by: Codex
llama.cpp
LLM inference in C/C++
ggml / ops / maintainer PRs / dev stats / lib llama API / llama-server REST API
Quick start
A few options to get llama.cpp installed on your machine:
- Visit https://llama.app and follow the instructions
- Run with Docker - see our Docker documentation
- Download pre-built binaries from the releases page
- Build from source by cloning this repository - check out our build guide
Once installed:
# 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 serve -hf ggml-org/Qwen3.5-0.8B-GGUF
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Description
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
- AVX, AVX2, AVX512 and AMX support for x86 architectures
- RVV, ZVFH, ZFH, ZICBOP and ZIHINTPAUSE support for RISC-V architectures
- 1.5-bit, 2-bit, 3-bit, 4-bit, 5-bit, 6-bit, and 8-bit integer quantization for faster inference and reduced memory use
- Custom CUDA kernels for running LLMs on NVIDIA GPUs (support for AMD GPUs via HIP and Moore Threads GPUs via MUSA)
- Vulkan and SYCL backend support
- CPU+GPU hybrid inference to partially accelerate models larger than the total VRAM capacity
The llama.cpp project is build on top of the ggml library.
Supported backends
| Backend | Target devices |
|---|---|
| BLAS | All |
| BLIS | All |
| CANN | Ascend NPU |
| CUDA | Nvidia GPU |
| HIP | AMD GPU |
| Hexagon [In Progress] | Snapdragon |
| IBM zDNN | IBM Z & LinuxONE |
| MUSA | Moore Threads GPU |
| Metal | Apple Silicon |
| OpenCL | Adreno GPU |
| OpenVINO [In Progress] | Intel CPUs, GPUs, and NPUs |
| RPC | All |
| SYCL | Intel GPU |
| VirtGPU | VirtGPU APIR |
| Vulkan | GPU |
| WebGPU | All |
| ZenDNN | AMD CPU |
Documentation
Tools
Development
- How to build
- Running on Docker
- Build on Android
- Multi-GPU usage
- Performance troubleshooting
- GGML tips & tricks
- XCFramework
- Completions
- Models
- Release process
Contributing
- Contributors can open PRs
- Collaborators will be invited based on contributions
- Maintainers can push to branches in the
llama.cpprepo and merge PRs into themasterbranch - Any help with managing issues, PRs and projects is very appreciated!
- Read the CONTRIBUTING.md for more information
Acknowledgements
- yhirose/cpp-httplib - Single-header HTTP server, used by
llama-server- MIT license - nothings/stb - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- mackron/miniaudio - Single-header audio format decoder, used by multimodal subsystem - Public domain
- sheredom/subprocess.h - Single-header process launching solution for C and C++ - Public domain