README

llama.cpp

!llama



LLM inference in C/C++








ggml / ops / maintainer PRs%20sort%3Aupdated-desc) / dev stats / lib llama API / llama-server REST API



 Quick start

A few options to get llama.cpp installed on your machine:

bash
 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:

sh
 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 | Huawei 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]](docs/backend/OPENVINO.md) | 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
- Release process

 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 - 

Top issues

Discussions

Latest release

Overview

This release adds OCP FP8 E4M3 quantization support, a new flash_attn_ext_rows op that makes the unified KV cache efficient for multi-sequence inference, and a generic batched tensor copy API. Four new architectures are supported (Clover, Limite, Metis-1.6 and Mistral Large 4), audio generation and transcription expand with Chatterbox TTS, Nemotron 3.5 Transcribe and predefined Pocket TTS voices, and the Gemma 4 E2B/E4B models get major prefill optimizations. ggml is updated to v0.29.0, bringing FP8 E4M3, flash attention rows, NCCL-based multi-node tensor parallelism and performanc