mudler/LocalAI

Golocalai.io

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

llamaaillmstable-diffusionapittsmusicgenmambaaudio-generationimage-generationtext-generationrerank

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아티팩트Go Modulesgo get github.com/mudler/LocalAI

README

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

A small LocalAI core with backends (llama.cpp, vLLM, MLX, whisper.cpp, stable-diffusion, kokoro, parakeet.cpp...) plugged in as separate on-demand images

Created by Ettore Di Giacinto and maintained by the LocalAI team.

:book: Documentation | :speech_balloon: Discord | 💻 Quickstart | 🖼️ Models | ❓FAQ

Guided tour

https://github.com/user-attachments/assets/08cbb692-57da-48f7-963d-2e7b43883c18

Click to see more!

Quickstart

macOS

Note: The DMG is not signed by Apple. After installing, run: sudo xattr -d com.apple.quarantine /Applications/LocalAI.app. See #6268 for details.

Containers (Docker, podman, ...)

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

CPU only:

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

NVIDIA GPU:

# 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

AMD GPU (ROCm):

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 (oneAPI):

docker run -ti --name local-ai -p 8080:8080 --device=/dev/dri/card1 --device=/dev/dri/renderD128 localai/localai:latest-gpu-intel

Vulkan GPU:

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

Loading models

# 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 test a running LocalAI server from the terminal, open an interactive chat session from another shell. Inside the prompt, /models lists installed models and /model <name> switches between them.

# 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.

Latest News

June 2026: New native biometric backends from the LocalAI team: voice-detect.cpp for speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion) and face-detect.cpp for face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace). Both are from-scratch C++/ggml engines with no Python or onnxruntime at inference, self-contained GGUF weights, bit-exact parity with the reference, and GPU cuDNN parity, replacing the heavier Python insightface and speaker-recognition backends (PR #10441).
June 2026: New realtime voice assistant demo (a tiny Go client for the Realtime API with a full talk-back voice loop and tool calling), plus streaming of the realtime LLM / TTS / transcription pipeline stages and configurable WebRTC ICE candidates.
June 2026: Big speech push: the parakeet.cpp ASR engine gains NeMo-faithful segment timestamps, a multilingual streaming Nemotron-3.5 model, dynamic batching for concurrent transcription and CUDA graphs; the new CrispASR backend adds multi-architecture ASR + TTS, and 60 Piper TTS voices across 42 languages land in the gallery (plus per-request TTS instructions and params).
June 2026: New backends and models: locate-anything.cpp for open-vocabulary object detection via ggml, Ideogram4 image generation in stablediffusion-ggml, llama.cpp video input, and the Gemma 4 QAT family with MTP speculative-decoding pairs. Plus an interactive CLI chat mode and RAG source citations in agent responses.
June 2026: Distributed mode hardening: prefix-cache-aware routing, a production-ready request router with auto-sized embedding/rerank batches, ds4 layer-split distributed inference, NATS JWT auth + TLS/mTLS, and resumable file uploads.
May 2026: LocalAI 4.3.0 - llama.cpp prompt cache on by default (repeated system prompts collapse from minutes to seconds), keyless cosign signing of backend OCI images, per-API-key + per-user usage attribution, Distributed v3 with per-request replica routing. Release notes
May 2026: LocalAI 4.2.0 - LocalAI sees and hears: voice recognition, face recognition + antispoofing liveness, speaker diarization. Plus drop-in Ollama API, video generation, redesigned UI with i18n + admin-configurable branding, vLLM at feature parity with llama.cpp, and 11 new backends. Release notes
April 2026: LocalAI 4.1.0 - LocalAI becomes a control tower: distributed cluster mode with VRAM-aware smart routing + autoscaling, multi-user platform with OIDC and API keys, per-user quotas with predictive analytics, in-UI fine-tuning with TRL (auto-export to GGUF), on-the-fly quantization backend, visual pipeline editor. Release notes
March 2026: LocalAI 4.0.0 - native agentic orchestration with the new Agenthub community hub, full React UI rewrite with Canvas mode, MCP Apps + client-side with tool streaming, WebRTC realtime audio, MLX-distributed. Release notes
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.

Features

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

Supported Backends & Acceleration

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.

Backends built by us

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:

Backend	What it does
parakeet.cpp	C++/GGML port of NVIDIA NeMo Parakeet ASR (tdt/ctc/rnnt/hybrid), with cache-aware streaming transcription
moss-transcribe.cpp	C++/GGML port of OpenMOSS MOSS-Transcribe-Diarize: joint long-form transcription, speaker diarization and timestamping in a single pass
moss-tts.cpp	C++/GGML port of the OpenMOSS MOSS-TTS family: text-to-speech (MOSS-TTS-Local v1.5, 48 kHz stereo) with reference-audio voice cloning, through the MOSS-Audio-Tokenizer neural codec
ced.cpp	C++/GGML port of the CED audio-tagging models: sound-event classification (527-class AudioSet) over REST and the realtime API for live recognition
voice-detect.cpp	Speaker recognition and voice analysis (ECAPA-TDNN, WeSpeaker, ERes2Net, CAM++, wav2vec2 age/gender/emotion), replacing the Python speaker-recognition backend
voxtral-tts.c	Voxtral Realtime 4B speech-to-text in pure C
vibevoice.cpp	Native port of Microsoft VibeVoice for TTS (voice cloning) and long-form ASR with speaker diarization
rf-detr.cpp	Native RF-DETR object detection and instance segmentation
locate-anything.cpp	Open-vocabulary object detection and visual grounding (LocateAnything-3B)
depth-anything.cpp	Depth Anything 3 monocular metric depth + camera pose estimation
face-detect.cpp	Face detection, recognition, demographics and anti-spoofing (SCRFD/ArcFace, YuNet/SFace), replacing the Python insightface backend
free-splatter.cpp	Pose-free 3D reconstruction (FreeSplatter): turns a handful of plain photos into 3D Gaussians, no camera poses or GPU required
privacy-filter.cpp	Standalone GGML PII/NER token-classification engine powering LocalAI's PII redaction tier
LocalVQE	Joint acoustic echo cancellation, noise suppression, and dereverberation
local-store	Local-first vector database for embeddings (shipped in-tree)

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.

Resources

Documentation
LLM fine-tuning guide
Build from source
Kubernetes installation
Integrations & community projects
Installation video walkthrough
Media & blog posts
Examples — including the realtime voice assistant demo (Go client for the Realtime API with tool calling)

Team

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.

Citation

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}},

Star history

License

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

Acknowledgements

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

Contributors

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