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mudler/LocalAI: :robot: The free, Open Source alternative to OpenAI, Claude and others. Self-hosted and local-first. Drop-in replacement for OpenAI, running on consumer-grade hardware. No GPU required. Runs gguf, transformers, diffusers and many more models architectures. Features: Generate Text, Audio, Video, Images, Voice Cloning, Distributed, P2P inference


πŸ’‘ Get help - ❓FAQ πŸ’­Discussions πŸ’¬ Discord πŸ“– Documentation website

πŸ’» Quickstart πŸ–ΌοΈ Models πŸš€ Roadmap πŸ₯½ Demo 🌍 Explorer πŸ›« Examples Try on

LocalAI is the free, Open Source OpenAI alternative. LocalAI act as a drop-in replacement REST API that's compatible with OpenAI (Elevenlabs, Anthropic... ) API specifications for local AI inferencing. It allows you to run LLMs, generate images, audio (and not only) locally or on-prem with consumer grade hardware, supporting multiple model families. Does not require GPU. It is created and maintained by Ettore Di Giacinto.

πŸ†• LocalAI is now part of a comprehensive suite of AI tools designed to work together:

A powerful Local AI agent management platform that serves as a drop-in replacement for OpenAI's Responses API, enhanced with advanced agentic capabilities.

A REST-ful API and knowledge base management system that provides persistent memory and storage capabilities for AI agents.

Run the installer script:

# Basic installation
curl https://localai.io/install.sh | sh

For more installation options, see Installer Options.

Or run with docker:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest
# CUDA 12.0
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-12

# CUDA 11.7
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-11

# NVIDIA Jetson (L4T) ARM64
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-gpu-hipblas
Intel GPU Images (oneAPI): 
docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-gpu-vulkan
AIO Images (pre-downloaded models): 
# CPU version
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-cpu

# NVIDIA CUDA 12 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-12

# NVIDIA CUDA 11 version
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-aio-gpu-nvidia-cuda-11

# Intel GPU version
docker run -ti --name local-ai -p 8080:8080 localai/localai:latest-aio-gpu-intel

# AMD GPU version
docker run -ti --name local-ai -p 8080:8080 --device=/dev/kfd --device=/dev/dri --group-add=video localai/localai:latest-aio-gpu-hipblas

For more information about the AIO images and pre-downloaded models, see Container Documentation.

To load models:

# From the model gallery (see available models with `local-ai models list`, in the WebUI from the model tab, or visiting https://models.localai.io)
local-ai run llama-3.2-1b-instruct:q4_k_m
# Start LocalAI with the phi-2 model directly from huggingface
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
# Install and run a model from the Ollama OCI registry
local-ai run ollama://gemma:2b
# Run a model from a configuration file
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
# Install and run a model from a standard OCI registry (e.g., Docker Hub)
local-ai run oci://localai/phi-2:latest

⚑ Automatic Backend Detection: When you install models from the gallery or YAML files, LocalAI automatically detects your system's GPU capabilities (NVIDIA, AMD, Intel) and downloads the appropriate backend. For advanced configuration options, see GPU Acceleration.

For more information, see πŸ’» Getting started

Roadmap items: List of issues

πŸ”— Community and integrations

Build and deploy custom containers:

WebUIs:

Model galleries

Other:

If you utilize this repository, data in a downstream project, please consider citing it with:

@misc{localai,
  author = {Ettore Di Giacinto},
  title = {LocalAI: The free, Open source OpenAI alternative},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/go-skynet/LocalAI}},

Do you find LocalAI useful?

Support the project by becoming a backer or sponsor. Your logo will show up here with a link to your website.

A huge thank you to our generous sponsors who support this project covering CI expenses, and our Sponsor list:


LocalAI is a community-driven project created by Ettore Di Giacinto.

MIT - Author Ettore Di Giacinto mudler@localai.io

LocalAI couldn't have been built without the help of great software already available from the community. Thank you!

This is a community project, a special thanks to our contributors! πŸ€—

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