llama.cpp
Inference in portable C++, from laptops to single-board machines
Overview
The quantisation work here is what put usable models on hardware without a datacentre GPU, and much of the local-model ecosystem is built on top of it. Working with it directly means dealing with build flags and hardware specifics that higher-level wrappers hide from you.
What can you do with llama.cpp?
- Run a model in one command — llama cli -hf ggml-org/Qwen3.5-0.8B-GGUF pulls the GGUF straight from Hugging Face and drops you into an interactive session, with no Python environment involved.
- Serve an OpenAI-compatible endpoint — llama serve -hf <repo> starts a REST server that existing OpenAI clients can be pointed at, and ships a built-in web UI for trying the model.
- Fit models larger than VRAM — Integer quantisation from 1.5-bit through 8-bit, combined with CPU+GPU hybrid inference, lets you offload only part of a model to the GPU and keep the rest in system RAM.
- Target the hardware you have — One source tree covers Metal and ARM NEON on Apple silicon, CUDA on NVIDIA, HIP on AMD, MUSA on Moore Threads, plus Vulkan, SYCL, OpenCL, WebGPU and CANN backends.
- Constrain output with GBNF grammars — A GBNF grammar file forces generation to match a syntax you define, which is how you get reliably structured output out of a small local model.
Documentation
Reproduced from the ggml-org/llama.cpp README, published under MIT. Read the original ↗
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-GGUFDescription
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 |