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* ci : create pre-release with change log and nightly link in make-release After pushing the tag, create a pre-release using ggml-org/action-create-release. The release description is generated by scripts/make-release-desc.sh: the change log between the current and previous version (one line per commit), a link to the corresponding nightly build when it exists, and a note that semantic versioning is still work in progress. Assisted-by: pi:llama.cpp/Qwen3.8-27B * cmake : bump version to 0.1.2 Assisted-by: pi:llama.cpp/Qwen3.8-27B * ci : find the nightly tag by commit in make-release-desc.sh The nightly release is guaranteed by the release checks to point at HEAD, so instead of reconstructing its name (commit count, branch, hash) just pick the b* tag pointing at HEAD. This also drops the RELEASE_BRANCH env var from the workflow. Assisted-by: pi:llama.cpp/Qwen3.8-27B * ci : resolve the release commit from the version tag in make-release-desc.sh The change log and nightly lookup now use the commit the version tag points at (HEAD when the tag does not exist), instead of always HEAD. This makes the script usable locally for older versions, e.g. ./scripts/make-release-desc.sh v0.1.1. The tag is resolved to a SHA first, since --points-at does not peel annotated tags. Assisted-by: pi:llama.cpp/Qwen3.8-27B * ci : normalize the version argument in make-release-desc.sh Accept the version with or without the leading v (0.1.1 == v0.1.1) and reject anything else, instead of silently treating a bare version as a non-existent tag name. Assisted-by: pi:llama.cpp/Qwen3.8-27B * cont : clean-up |
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llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / compile times / 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 - stb-image - Single-header image format decoder, used by multimodal subsystem - Public domain
- nlohmann/json - Single-header JSON library, used by various tools/examples - MIT License
- miniaudio.h - Single-header audio format decoder, used by multimodal subsystem - Public domain
- subprocess.h - Single-header process launching solution for C and C++ - Public domain