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* ci : apply ccache-clear with older/min/dry-run to all ccache jobs Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : install gh in ccache-clear if missing (container jobs) The ccache-clear action relies on the gh CLI, which is not present in container-based jobs. Install it on demand so those jobs can clear caches. Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : install gh via apt repo in ccache-clear The install.sh script used previously is no longer served (404). Switch to the official GitHub CLI apt repository, which is still available. Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : pass --repo to gh cache commands in ccache-clear In container jobs gh cannot auto-detect the repository from git, so gh cache list/delete fail with 'failed to run git: not a git repository'. Pass the repository explicitly via --repo using GITHUB_REPOSITORY. Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : drop -new suffix from vulkan ccache key The -new suffix was only needed to force a fresh cache. With ccache-clear now evicting stale caches, the original key can be used again. The old ccache-vulkan-ubuntu-24.04-arm-new entries still match the ccache-clear key prefix and are cleaned up automatically. Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : fix ccache-clear date parsing on macOS (BSD date) macOS ships BSD date, which has no -d option. The older cutoff check was silently disabled there: 'date: illegal option -- d' errors in the log and the loop was only stopped by the min limit, risking deletion of caches not older than the cutoff (e.g. saved by a concurrent job). Parse the ISO-8601 timestamps with GNU date when available and fall back to BSD date otherwise (TZ=UTC, fractional seconds dropped). Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : extract ccache-clear logic into scripts/ccache-clear.sh The composite action now consists of a dedicated step that installs the GitHub CLI when missing (e.g. in container jobs) and a thin step that calls the new script. The script follows the make-release-checks.sh conventions (usage/env header, set -euo pipefail, CLI flags) and only checks that gh is available. The action inputs are unchanged, so the workflow steps are untouched. Assisted-by: llama.cpp:DeepSeek-v4-Flash-0731 * ci : remove unused apple ccaches |
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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 [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 - 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