LocalAI is the open-source AI engine. Run any model - LLMs, vision, voice, image, video - on any hardware. No GPU required.

• Drop-in API compatibility — OpenAI, Anthropic, ElevenLabs APIs
• 36+ backends — llama.cpp, vLLM, transformers, whisper, diffusers, MLX...
• Any hardware — NVIDIA, AMD, Intel, Apple Silicon, Vulkan, or CPU-only
• Multi-user ready — API key auth, user quotas, role-based access
• Built-in AI agents — autonomous agents with tool use, RAG, MCP, and skills
• Privacy-first — your data never leaves your infrastructure

Created and maintained by Ettore Di Giacinto.

📖 Documentation | 💬 Discord | 💻 Quickstart | 🖼️ Models | ❓FAQ

Note: The DMG is not signed by Apple. After installing, run: sudo xattr -d com.apple.quarantine /Applications/LocalAI.app. See

Already ran LocalAI before? Use docker start -i local-ai to restart an existing container.

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest

# CUDA 13
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-gpu-nvidia-cuda-13

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

# NVIDIA Jetson ARM64 (CUDA 12, for AGX Orin and similar)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64

# NVIDIA Jetson ARM64 (CUDA 13, for DGX Spark)
docker run -ti --name local-ai -p 8080:8080 --gpus all localai/localai:latest-nvidia-l4t-arm64-cuda-13

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

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

# From the model gallery (see available models with `local-ai models list` or at https://models.localai.io)
local-ai run llama-3.2-1b-instruct:q4_k_m
# From Huggingface
local-ai run huggingface://TheBloke/phi-2-GGUF/phi-2.Q8_0.gguf
# From the Ollama OCI registry
local-ai run ollama://gemma:2b
# From a YAML config
local-ai run https://gist.githubusercontent.com/.../phi-2.yaml
# From a standard OCI registry (e.g., Docker Hub)
local-ai run oci://localai/phi-2:latest

Automatic Backend Detection: LocalAI automatically detects your GPU capabilities and downloads the appropriate backend. For advanced options, see GPU Acceleration.

For more details, see the Getting Started guide.

• March 2026: Agent management, New React UI, WebRTC, MLX-distributed via P2P and RDMA, MCP Apps, MCP Client-side
• February 2026: Realtime API for audio-to-audio with tool calling, ACE-Step 1.5 support
• January 2026: LocalAI 3.10.0 — Anthropic API support, Open Responses API, video & image generation (LTX-2), unified GPU backends, tool streaming, Moonshine, Pocket-TTS. Release notes
• December 2025: Dynamic Memory Resource reclaimer, Automatic multi-GPU model fitting (llama.cpp), Vibevoice backend
• November 2025: Import models via URL, Multiple chats and history
• October 2025: Model Context Protocol (MCP) support for agentic capabilities
• September 2025: New Launcher for macOS and Linux, extended backend support for Mac and Nvidia L4T, MLX-Audio, WAN 2.2
• August 2025: MLX, MLX-VLM, Diffusers, llama.cpp now supported on Apple Silicon
• July 2025: All backends migrated outside the main binary — lightweight, modular architecture

For older news and full release notes, see GitHub Releases and the News page.

• Text generation (llama.cpp, transformers, vllm ... and more)
• Text to Audio
• Audio to Text
• Image generation
• OpenAI-compatible tools API
• Realtime API (Speech-to-speech)
• Embeddings generation
• Constrained grammars
• Download models from Huggingface
• Vision API
• Object Detection
• Reranker API
• P2P Inferencing
• Distributed Mode — Horizontal scaling with PostgreSQL + NATS
• Model Context Protocol (MCP)
• Built-in Agents — Autonomous AI agents with tool use, RAG, skills, SSE streaming, and Agent Hub
• Backend Gallery — Install/remove backends on the fly via OCI images
• Voice Activity Detection (Silero-VAD)
• Integrated WebUI

LocalAI supports 36+ backends including llama.cpp, vLLM, transformers, whisper.cpp, diffusers, MLX, MLX-VLM, and many more. Hardware acceleration is available for NVIDIA (CUDA 12/13), AMD (ROCm), Intel (oneAPI/SYCL), Apple Silicon (Metal), Vulkan, and NVIDIA Jetson (L4T). All backends can be installed on-the-fly from the Backend Gallery.

See the full Backend & Model Compatibility Table and GPU Acceleration guide.

• Documentation
• LLM fine-tuning guide
• Build from source
• Kubernetes installation
• Integrations & community projects
• Installation video walkthrough
• Media & blog posts
• Examples

LocalAI is helped being maintained by a team of autonomous AI agents led by an AI Scrum Master.

• Live Reports: reports.localai.io
• Project Board: Agent task tracking
• Blog Post: Learn about the experiment

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:

A special thanks to individual sponsors, a full list is on GitHub and buymeacoffee. Special shout out to drikster80 for being generous. Thank you everyone!

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!

• llama.cpp
• https://github.com/tatsu-lab/stanford_alpaca
• https://github.com/cornelk/llama-go for the initial ideas
• https://github.com/antimatter15/alpaca.cpp
• https://github.com/EdVince/Stable-Diffusion-NCNN
• https://github.com/ggerganov/whisper.cpp
• https://github.com/rhasspy/piper
• exo for the MLX distributed auto-parallel sharding implementation

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