8bb3cf0d21
* hexagon: move the converting CPY in blocks of rows per DMA descriptor so short rows are not DMA-latency bound * hex-cpy: cleanup cpy cursor handling * hex-cpy-concat: introduce threaded dma/hvx gather-row helper Co-authored-by: Ruslan Karymov <67752306+karusrus@users.noreply.github.com> * hex-cpy: remove leftover rejections for extended buffers * hex-cpy: relax the gate for fast-path * hex-cpy: remove scalar divs and vtcm access and use explicit gather_sync * hex-cpy: add proper support for mdev work splitting to fastpath * hex-cpy: optimize dense gather-row tasks * hex-cpy: unroll gather-rows loops * hex-cpy: add fastpath for f16 * hex-cpy-concat: support for colapsing 3/4D dims * hex-cpy: double-buffer support * hex-cpy: factor out gather-rows into more of an op by itself * hex-cpy-concat: dedup kernel-params * hex-cpy-concat: step-down nr to fit instead of hard fail * hex-gather: replace div with a mask * hex-gather-rows: cleanup dma pipeline and remove the stalls * hex-gather: specialize compute funcs * hex-gather: reduce scalar reg.spills and func calls * hex-gather: do not recompute rows-per-thread on the npu * hex-build: add support for building/installing just hexagon * hex-cpy-concat: remove redundant barrier in concat and move cpy barrier under fence * hex-gather: annotations for dma-out * hex-mdev: make all key vtcm_acquire sync state atomic (fixes random hangs in mdev setups) --------- Co-authored-by: Yiwei Shao <yiwei@aizip.ai> Co-authored-by: Ruslan Karymov <67752306+karusrus@users.noreply.github.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:
# 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
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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