Max Krasnyansky
b797b9ca81
* hex-repack: Remove need_repack hack and streamline the rules -- breaks GET_ROWS tests for K-Quants, will fix in the next round Add support for GET_ROWS with tiled Q5_K Add support for raw k-quant tensors * hex-get-rows: vectorize raw k-quant ukernels * hex-binary: refactor dma-loop thread functions (no need for macros, etc) * hex-quant: move/cleanup flat and tiled dequant ukernels * hex-repack: add support for repacking/tiling F16/F32/BF16 2D weights * hex-get-rows: remove redundant cast * hex-repack: proper filtering for BF16 that were leaking through during the tests * hex-repack: streamling tiling rules (broken MM for now) * hex-repack: fix mmid after tiling updates * hex-repack: rename repacked to tiled in all flags/funcs/kernels/vars * hex-mm: fix valid_rows calc in various kernels * hex-mm: remove unused hvx_mm_nx_2d_flat * hex-mm: cleanup and optimize hvx-tiled vec_dot_f16 * hex-mm: another pass at hvx-tiled f16 matmul * hex-get-rows: optimize small src1 * hex-get-rows: one more pass at recovering get-rows perf regression * hex-repack: export GGML_HEXAGON_RT_REPACK via run.py * hex-mm-tiled: fix accuracy regression with hmx-tiled-f16 matmul * hex-mm: fold tiled_f16 checks into the general flow * hex-get-rows: remove dead checks for buffer_is_hexagon and tiling during supports_op * hex-quant: proper tail handling in new dequant ukernels * hex-get-rows: remove unused code and cleanup support checks * hex-mm: cleanup ne1 padding/tiling checks * hex-mm: cleanup fusion checks for tiled tensors * hex-repack: add a little can_repack helper to reduce duplication * hex-repack: never repack host buffers * hex-repack: factor out repack functions into separate .h and .cpp * hex-tensor: cleanup tensor ifaces for tiled/padded sizes * hex-repack: update k-quant tiling comments and cleanup #defines * hex-dcache: factor out l2/dcache dirty tracking and flushing * hex-tensor: update docs and remove #defs duplication * hex-tensor: remove unused/incorrect get_data interface * hex-get-rows: cleanup vtcm layout build * tests: disable huge MM tests * hex-dcache: add missing header
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:
# curl
curl -LsSf https://llama.app/install.sh | sh
# powershell
irm https://llama.app/install.ps1 | iex
- 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
|
|
|
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