mirror of
https://github.com/ggml-org/llama.cpp.git
synced 2026-09-14 18:02:52 +02:00
Ruben OrtlamandPiotr Wilkin
8c7611aa2f
* vulkan: add int8 coopmat quantized matmul shader * apply scales inline * use scalar sums * probe and directly access coopmat values instead of going through shmem * add q8_0 support * add BK_STEP to shader, default to 2 * use larger workgroups * double buffering * preload scales * coopmat load first, then wmma * use float for scales * add faster RDNA int->float conversion * workgroup scheduling for cache proximity * clean up * use wave32 * restructure for vgpr use * skip computation for inactive tiles * only force subgroup size 32 on AMD RDNA * use BK_STEP 4 * fix compilation * move quant-specific prefetch function out of main file * add q4_1, q5_0, q5_1 support * restructure mmq cm1 functions * enable mul_mat_id support * fix segfault * fix mul_mat_id bug * support iq4_nl and mxfp4 * remove elem row/col fast path, invalid for RDNA4 * use shmem arrays for LUTs * use 4-byte loads where possible * add q3_k, q4_k, q5_k, q6_k and nvfp4 support * fix l warptile * improve performance * improve performance * improvements * dedup b scales * merge shmem arrays * undo uint8_t, gate to RDNA3/4 * add RDNA4 architecture, use for hardcoded coopmat elem thread access, set BK_STEP back to 4 * improve offset application * clean up * fix iq4_nl and nvfp4 performance * rdna4 tuning * use BK_STEP 2 on MUL_MAT_ID * adapt to upstream changes * fix shmem support function, clean up comments * fix warptile logic Co-authored-by: Piotr Wilkin (ilintar) <piotr.wilkin@syndatis.com>
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 | 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