llama.cpp

Important

This is the PrismML fork of llama.cpp, the main line behind the Bonsai models (branch prism, developed as prism-v7). It tracks current mainline llama.cpp and adds the fork's low-bit formats and runtime features on top.

New here? Start with the Bonsai-demo repo. It downloads the right models and the correct prebuilt binaries for your hardware/backend automatically.

Which ternary model file to use:

• *-PQ2_0.gguf (fork group-128, ggml id 142): preferred on Metal, CUDA, HIP and CPU. About 6% smaller than group-64.
• *-Q2_0_g64.gguf / 27B *-Q2_g64.gguf (official group-64, ggml id 42): runs on every backend here AND on mainline llama.cpp. If unsure, use this. Newer model releases name this file plain *-Q2_0.gguf.
• *-Q2_0.gguf on OLDER model repos is the deprecated legacy format (group 128 stored as id 42). It does not load on these builds; the error tells you which file to get instead. If you must run it, use the frozen prism-v5 line and its final release prism-b9601.

Speculative decoding (dspark) is supported via mainline's draft-dspark plus fork patches. Drafters published for older model releases need a one-time conversion with gguf-dspark-to-dflash (see SPECULATIVE.md in Bonsai-demo); newer releases ship ready-to-use drafters.

Do NOT build from prism-v6 (stale mid-migration snapshot) and do NOT mix this fork's ggml-* libraries with a stock llama.cpp build.

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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

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
• 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 - 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