Quick start

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

Description

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.

Supported backends

Obtaining and quantizing models

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, such as ModelScope, 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. For example, you may opt to downloading model checkpoints from ModelScope or other model sharing communities by setting the environment variable, e.g. MODEL_ENDPOINT=https://www.modelscope.cn/.

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: https://github.com/ggml-org/llama.cpp/discussions/10123)
• Use the GGUF-editor space to edit GGUF meta data in the browser (more info: https://github.com/ggml-org/llama.cpp/discussions/9268)
• Use the Inference Endpoints to directly host llama.cpp in the cloud (more info: https://github.com/ggml-org/llama.cpp/discussions/9669)

To learn more about model quantization, read this documentation

llama-cli

A CLI tool for accessing and experimenting with most of llama.cpp's functionality.

• 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/

llama-server

A lightweight, OpenAI API compatible, HTTP server for serving LLMs.

• 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

  # use the /reranking endpoint
  llama-server -m model.gguf --reranking
  

• Constrain all outputs with a grammar

  # custom grammar
  llama-server -m model.gguf --grammar-file grammar.gbnf
  
  # JSON
  llama-server -m model.gguf --grammar-file grammars/json.gbnf
  

llama-perplexity

A tool for measuring the perplexity ¹ (and other quality metrics) of a model over a given text.

• Measure the perplexity over a text file

  llama-perplexity -m model.gguf -f file.txt
  
  # [1]15.2701,[2]5.4007,[3]5.3073,[4]6.2965,[5]5.8940,[6]5.6096,[7]5.7942,[8]4.9297, ...
  # Final estimate: PPL = 5.4007 +/- 0.67339
  

• Measure KL divergence

  # TODO
  

llama-bench

Benchmark the performance of the inference for various parameters.

• Run default benchmark

  llama-bench -m model.gguf
  
  # Output:
  # | model               |       size |     params | backend    | threads |          test |                  t/s |
  # | ------------------- | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |
  # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         pp512 |      5765.41 ± 20.55 |
  # | qwen2 1.5B Q4_0     | 885.97 MiB |     1.54 B | Metal,BLAS |      16 |         tg128 |        197.71 ± 0.81 |
  #
  # build: 3e0ba0e60 (4229)
  

llama-simple

A minimal example for implementing apps with llama.cpp. Useful for developers.

• Basic text completion

  llama-simple -m model.gguf
  
  # Hello my name is Kaitlyn and I am a 16 year old girl. I am a junior in high school and I am currently taking a class called "The Art of
  

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!
• See good first issues for tasks suitable for first contributions
• Read the CONTRIBUTING.md for more information
• Make sure to read this: Inference at the edge
• A bit of backstory for those who are interested: Changelog podcast

Other documentation

• cli
• completion
• server
• GBNF grammars

Development documentation

• How to build
• Running on Docker
• Build on Android
• Performance troubleshooting
• GGML tips & tricks

Seminal papers and background on the models

If your issue is with model generation quality, then please at least scan the following links and papers to understand the limitations of LLaMA models. This is especially important when choosing an appropriate model size and appreciating both the significant and subtle differences between LLaMA models and ChatGPT:

• LLaMA:
  • Introducing LLaMA: A foundational, 65-billion-parameter large language model
  • LLaMA: Open and Efficient Foundation Language Models
• GPT-3
  • Language Models are Few-Shot Learners
• GPT-3.5 / InstructGPT / ChatGPT:
  • Aligning language models to follow instructions
  • Training language models to follow instructions with human feedback

XCFramework

The XCFramework is a precompiled version of the library for iOS, visionOS, tvOS, and macOS. It can be used in Swift projects without the need to compile the library from source. For example:

// swift-tools-version: 5.10
// The swift-tools-version declares the minimum version of Swift required to build this package.

import PackageDescription

let package = Package(
    name: "MyLlamaPackage",
    targets: [
        .executableTarget(
            name: "MyLlamaPackage",
            dependencies: [
                "LlamaFramework"
            ]),
        .binaryTarget(
            name: "LlamaFramework",
            url: "https://github.com/ggml-org/llama.cpp/releases/download/b5046/llama-b5046-xcframework.zip",
            checksum: "c19be78b5f00d8d29a25da41042cb7afa094cbf6280a225abe614b03b20029ab"
        )
    ]
)

The above example is using an intermediate build b5046 of the library. This can be modified to use a different version by changing the URL and checksum.

Completions

Command-line completion is available for some environments.

Bash Completion

$ build/bin/llama-cli --completion-bash > ~/.llama-completion.bash
$ source ~/.llama-completion.bash

Optionally this can be added to your .bashrc or .bash_profile to load it automatically. For example:

$ echo "source ~/.llama-completion.bash" >> ~/.bashrc

Dependencies

• 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