mudler/LocalAI

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

A small core, not a bundle. Each backend wraps a best-in-class engine (llama.cpp, vLLM, whisper.cpp, stable-diffusion, MLX...) in its own image, pulled only when a model needs it. You install nothing you don't use.

• Composable by design: backends are separate and pulled on demand, so you install only what your model needs
• Open and extensible: load any model, or build your own backend in any language against an open interface
• Drop-in API compatibility: OpenAI, Anthropic, and ElevenLabs APIs across every backend
• Any model, any modality: LLMs, vision, voice, image, and video behind one API
• 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 by Ettore Di Giacinto and maintained by the LocalAI team.

📖 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 #6268 for details.

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

To work with a running LocalAI server from the terminal, start the built-in agent from another shell. It answers questions, reads your files and runs commands on your machine, asking you to approve anything that changes state. Inside a session, /models lists installed models and /model <name> switches between them. See the Terminal agent docs.

# Terminal 1
local-ai run llama-3.2-1b-instruct:q4_k_m

# Terminal 2
local-ai chat --model llama-3.2-1b-instruct:q4_k_m

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.

• October 2026: LocalAI 4.11.0 — Audio scenes with transcription, speaker labels, and sound detection. Also the Decisions API (/v1/systemone), model failover chains, signed OCI galleries, and Kimodo text-to-animation. Release notes
• September 2026: LocalAI 4.10.0 — A fleet operations dashboard, one credentials file for private model sources, and local-ai benchmark for measuring model latency and throughput. Release notes
• August 2026: LocalAI 4.9.0 — Deny-by-default authentication, opt-in chat context compression, unified model and backend management, and MiniMax-H3 video generation with audio. Release notes
• August 2026: LocalAI 4.8.0 — Introducing vllm.cpp (alpha development builds), image-to-3D generation with a built-in viewer, and audio.cpp for speech, transcription, VAD, diarization, source separation and sound generation. Release notes
• July 2026: LocalAI 4.7.0 — Managed voice cloning profiles, local video and talking-avatar generation, and interleaved reasoning with tool calls. Release notes
• July 2026: LocalAI 4.6.0 — AMD ROCm fixes, predictable realtime pipeline warmup, conversation forking, and more reliable distributed model loading. Release notes
• June 2026: LocalAI 4.5.0 — Native depth estimation and sound-event detection, NER-based PII filtering, and speaker-aware realtime conversations with automatic history compaction. Release notes

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

• 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 60+ backends including llama.cpp, vLLM, SGLang, 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.

Most backends wrap a best-in-class upstream engine. A handful of them are native C/C++/GGML engines (no Python at inference) developed and maintained by the LocalAI project itself:

We also maintain apex-quant, a per-tensor, per-layer quantization recipe for Mixture-of-Experts models that exploits their structural sparsity to produce GGUFs matching or beating Q8_0 quality - and they run out of the box on stock llama.cpp.

• Documentation
• LLM fine-tuning guide
• Build from source
• Kubernetes installation
• Integrations & community projects
• Installation video walkthrough
• Blog: release write-ups, benchmarks and engineering notes
• Examples — including the realtime voice assistant demo (Go client for the Realtime API with tool calling)

LocalAI is maintained by a small team of humans, together with the wider community of contributors.

• Ettore Di Giacinto — original author and project lead
• Richard Palethorpe — maintainer

A huge thank you to everyone who contributes code, reviews PRs, files issues, and helps users in Discord — LocalAI is a community-driven project and wouldn't exist without you. See the full contributors list.

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 and maintained by the LocalAI team.

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!