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* cmake : introduce semantic versioning (wip) This commit introduces semantic versioning to llama.cpp. * squash! cmake : introduce semantic versioning (wip) * cmake : update test-cmake README notes [no ci] * include libmtmd in output so show its semversioned * ci : add make-release workflow * ci : fix build number check in build-cmake-pkg.yml * examples : remove trailing whitespace * ci : abort if upstream ggml version does not exist * ci : extract step contents into scripts * ci : add GGML_NATIVE=OFF to ubuntu job * examples : remove CI build information from test-cmake [no ci] This commit removes the nightly/release information that I added previously to keep this focused only on using building and installing llama.cpp with cmake and being able to quickly verify changes or troubleshoot issues. * ci : merge scripts into single script * remove -dev-build_number support This commit removes the incremental build number (versioning) support that I added. This was incorrect and we should only use the semver for the version. Releases will be tag a nightly build and package maintainers/managers that build from source can use the tag and it is therefor important that the correct version is reported. So a nightly-build will report the semver without the build number. The build number and commit as availble via cmake and test-cmake has been updated to include an example of using them: ```console $ ./build.sh [test-cmake] version: 0.1.0, build: 10360 (08c69e381) ... ``` Refs: https://github.com/ggml-org/llama.cpp/pull/26839#discussion_r3755836969 * docs: add initial release.md documentation * cmake : clean-up and add LLAMA_BUILD_IS_DEV option * ci : remove version input from make-release job * ci : add LLAMA_BUILD_IS_DEV=OFF to build-cmake-pkg.yml Refs: https://github.com/danbev/llama.cpp/actions/runs/31576801921/job/94050639145 * docs : update release notes with LLAMA_BUILD_IS_DEV info [no ci] * ci : add TODO to winget workflow [no ci] --------- Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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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