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llama.cpp
LLM inference in C/C++
manifesto / ggml / ops / maintainer PRs / dev branches / 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
VLM session with llama cli Built-in web UI against llama serve
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
* cli
* completion
* server
* GBNF grammars
Development
* How to build
* Running on Docker
* Build on Android
* Multi-GPU usage
* Performance troubleshooting
* GGML tips & tricks
* XCFramework
* Completions
* Models
Contributing
* Contributors can open PRs
* Collaborators will be invited based on contributions
* Maintainers can push to branches in the llama.cpp repo and merge PRs into the master branch
* 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
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LLM inference in C/C++
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