Manifesto / ggml / ops

LLM inference in C/C++

• Changelog for libllama API
• Changelog for llama-server REST API

• Hugging Face cache migration: models downloaded with -hf are now stored in the standard Hugging Face cache directory, enabling sharing with other HF tools.
• guide : using the new WebUI of llama.cpp
• guide : running gpt-oss with llama.cpp
• [FEEDBACK] Better packaging for llama.cpp to support downstream consumers 🤗
• Support for the gpt-oss model with native MXFP4 format has been added | PR | Collaboration with NVIDIA | Comment
• Multimodal support arrived in llama-server: #12898 | documentation
• VS Code extension for FIM completions: https://github.com/ggml-org/llama.vscode
• Vim/Neovim plugin for FIM completions: https://github.com/ggml-org/llama.vim
• Hugging Face Inference Endpoints now support GGUF out of the box! #9669
• Hugging Face GGUF editor: discussion | tool

Getting started with llama.cpp is straightforward. Here are several ways to install it on your machine:

• Install llama.cpp using brew, nix or winget
• 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, you'll need a model to work with. Head to the Obtaining and quantizing models section to learn more.

Example command:

# Use a local model file
llama-cli -m my_model.gguf

# Or download and run a model directly from Hugging Face
llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

# Launch OpenAI-compatible API server
llama-server -hf ggml-org/gemma-3-1b-it-GGUF

The main goal of llama.cpp is to enable LLM 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 the main playground for developing new features for the ggml library.

The Hugging Face platform hosts a number of LLMs compatible with llama.cpp:

• Trending
• LLaMA

You can either manually download the GGUF file or directly use any llama.cpp-compatible models from Hugging Face or other model hosting sites, by using this CLI argument: -hf <user>/<model>[:quant]. For example:

llama-cli -hf ggml-org/gemma-3-1b-it-GGUF

By default, the CLI would download from Hugging Face, you can switch to other options with the environment variable MODEL_ENDPOINT. The MODEL_ENDPOINT must point to a Hugging Face compatible API endpoint.

After downloading a model, use the CLI tools to run it locally - see below.

llama.cpp requires the model to be stored in the GGUF file format. Models in other data formats can be converted to GGUF using the convert_*.py Python scripts in this repo.

The Hugging Face platform provides a variety of online tools for converting, quantizing and hosting models with llama.cpp:

• Use the GGUF-my-repo space to convert to GGUF format and quantize model weights to smaller sizes
• Use the GGUF-my-LoRA space to convert LoRA adapters to GGUF format (more info: #10123)
• Use the GGUF-editor space to edit GGUF meta data in the browser (more info: #9268)
• Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: #9669)

To learn more about model quantization, read this documentation

• Run in conversation mode

  Models with a built-in chat template will automatically activate conversation mode. If this doesn't occur, you can manually enable it by adding -cnv and specifying a suitable chat template with --chat-template NAME

  llama-cli -m model.gguf
  
  # > hi, who are you?
  # Hi there! I'm your helpful assistant! I'm an AI-powered chatbot designed to assist and provide information to users like you. I'm here to help answer your questions, provide guidance, and offer support on a wide range of topics. I'm a friendly and knowledgeable AI, and I'm always happy to help with anything you need. What's on your mind, and how can I assist you today?
  #
  # > what is 1+1?
  # Easy peasy! The answer to 1+1 is... 2!

• Run in conversation mode with custom chat template

  # use the "chatml" template (use -h to see the list of supported templates)
  llama-cli -m model.gguf -cnv --chat-template chatml
  
  # use a custom template
  llama-cli -m model.gguf -cnv --in-prefix 'User: ' --reverse-prompt 'User:'

• Constrain the output with a custom grammar

  llama-cli -m model.gguf -n 256 --grammar-file grammars/json.gbnf -p 'Request: schedule a call at 8pm; Command:'
  
  # {"appointmentTime": "8pm", "appointmentDetails": "schedule a a call"}

  The grammars/ folder contains a handful of sample grammars. To write your own, check out the GBNF Guide.

  For authoring more complex JSON grammars, check out https://grammar.intrinsiclabs.ai/

• Start a local HTTP server with default configuration on port 8080

  llama-server -m model.gguf --port 8080
  
  # Basic web UI can be accessed via browser: http://localhost:8080
  # Chat completion endpoint: http://localhost:8080/v1/chat/completions

• Support multiple-users and parallel decoding

  # up to 4 concurrent requests, each with 4096 max context
  llama-server -m model.gguf -c 16384 -np 4

• Enable speculative decoding

  # the draft.gguf model should be a small variant of the target model.gguf
  llama-server -m model.gguf -md draft.gguf

• Serve an embedding model

  # use the /embedding endpoint
  llama-server -m model.gguf --embedding --pooling cls -ub 8192

• Serve a reranking model

  Live data via GitHub REST API · Cached 30 min · Created 11 Mar 2023