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mudler's LocalAI [bot]andEttore Di Giacinto
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* docs(blog): final figures for the 4.8 post, and the MLX provider The cycle closed at 374 PRs over twenty-one days, not the 321 over eighteen the post was written against. Corrects the summary, the opening line, the contributor count and the gallery total, and moves the date to the day the release is cut. Adds the MLX GEMM provider (#11137), which merged after the post was written and is the one number an Apple Silicon reader wants: 1.54x to 2.19x on an M4 with time to first token roughly halving, both arms toggled on one binary. The +/-10% caveat travels with the table rather than being left in the PR. Two lines edited against the no-ai-slop skill while I was in the file, the same pass #11324 ran over the engines post: - The opener balanced two clauses across a colon and closed on "without lying to you", which is the built-to-be-quoted shape readers picked out of the HN thread. It is a flat statement now. - "This is a new modality rather than a new backend under an existing one" is a binary contrast that says nothing the next clause does not. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Read] [Edit] [Bash] * docs(blog): call vllm.cpp alpha, and finish the no-ai-slop pass vllm.cpp is not a released backend and the post read like it was. The old wording buried the caveat in a block quote at the end of the section and still said "first release of a young engine". It now says plainly, before the caveat can be skipped, that these are alpha development builds, that shipping them in 4.8 is about letting people try the thing rather than recommending it, and that llama-cpp stays the default. Also completes the no-ai-slop pass I had only half run. Counting the lines built to be quoted, headings and section endings included, the post is in reasonable shape: long flat informational stretches, tables followed by a plain finding, headings that are labels rather than epigram-verdicts. Three patterns survived, each one an item in eval.md: - "and inverts that:" set the usual shape against ours across a colon. The sentence works without the frame. - "Two things were conflated there: a signal, which needs one line, and the detail, which needs somewhere to put it" is a role-assignment pair. Says what happens instead. - "The maturity statement from the release notes is worth repeating in full" is throat-clearing in front of a quote, and the quote is gone. Left the rest alone. Minimum effective edit, not a rewrite. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Read] [Edit] [Bash] * docs(blog): present vllm.cpp as a community project, with its own numbers The post described vllm.cpp as "a from-scratch port of vLLM, written and maintained by the LocalAI team". Two things wrong with that. It is a community project, and it has stopped being only a port: it loads GGUF, runs on CPU, Metal and Vulkan, ships speculative decoding and KV offload, and its benchmark page measures against llama.cpp, MLX-LM and DwarfStar as well as vLLM, because those are the engines it competes with on that hardware. vLLM's role is now stated for what it is, the reference implementation. Correctness is checked against it and the scoreboard is kept against it. Also flags that the name will probably change, since it is drifting far enough that vllm.cpp will eventually mislead. Adds real numbers from the project's own docs/BENCHMARKS.md rather than adjectives: 1.045x vLLM at concurrency 1 on Qwen3.6-27B NVFP4 with token-for-token identical output, 1.010x and 1.013x at c16 and c32 on the 35B MoE and behind below that, prefill 1.18x over llama.cpp on CPU aarch64, 97.6% of MLX-LM warm total on an M4. Upstream's own caution travels with them: it treats c2 through c32 as ties because its noise band is 0.5% and those margins are 0.7% to 1.7%. Every figure was checked against ~/_git/vllm.cpp/docs/BENCHMARKS.md rather than restated from memory. The heading is marked alpha to match the section body. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Read] [Edit] [Bash] * docs(blog): say who maintains vllm.cpp, and add the DeepSeek Flash result Two corrections to the previous commit. "A community project" says nothing and was not quite true either. The LocalAI team maintains vllm.cpp. Community-first is the intent, not a description, so it now says that and says what backs it: its own repository, its own docs, benchmark record and issue tracker, and it runs without LocalAI anywhere in the picture. Adds the DeepSeek-V4-Flash result, which makes the divergence point better than any of the prose around it. That model does not run on vLLM on a single GB10: every vLLM-loadable checkpoint is 156 GB or more against a 119 GiB unified pool, and the only quant that fits is an extreme-low-bit GGUF that vLLM cannot load. vllm.cpp reads GGUF and runs it at 16.28 tok/s against ds4's 16.33, a parity result. Also notes MTP speculative decoding, token-identical to vLLM's and about 4% faster at concurrency 1. Both figures checked against ~/_git/vllm.cpp/docs/BENCHMARKS.md. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Read] [Edit] [Bash] * docs(blog): lead the DeepSeek result with what we run, not with what vLLM cannot The previous version opened on "that model does not run on vLLM on a single GB10 at all". Wrong emphasis twice over: it makes a strong negative claim about another project the headline, and it buries the actual result, which is that vllm.cpp runs DeepSeek-V4-Flash at roughly 2-bit (IQ2_XXS mixed, about 80 GB) on a single DGX Spark and decodes at 16.28 tok/s against DwarfStar's 16.33. The size constraint is still there, stated as the reason the quant is what it is rather than as a point about vLLM: at 300B+ total parameters even a 4-bit checkpoint is 156 GB or more, so a 2-bit GGUF is what fits the Spark's 119 GiB unified pool. The table row now names the quant and the box (IQ2_XXS, one DGX Spark) instead of just "GGUF, GB10", since that is the part a reader with a Spark wants. Figures unchanged and still from ~/_git/vllm.cpp/docs/BENCHMARKS.md. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Read] [Edit] [Bash] * docs(blog): say the new name is undecided "The name will probably change at some point" invited the obvious question. It now says the rename is expected and the name is still to be decided, which is the actual state. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude Code:claude-opus-5 [Read] [Edit] [Bash] --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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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
Guided tour
https://github.com/user-attachments/assets/08cbb692-57da-48f7-963d-2e7b43883c18
Click to see more!
User and auth
https://github.com/user-attachments/assets/228fa9ad-81a3-4d43-bfb9-31557e14a36c
Agents
https://github.com/user-attachments/assets/6270b331-e21d-4087-a540-6290006b381a
Usage metrics per user
https://github.com/user-attachments/assets/cbb03379-23b4-4e3d-bd26-d152f057007f
Fine-tuning and Quantization
https://github.com/user-attachments/assets/5ba4ace9-d3df-4795-b7d4-b0b404ea71ee
WebRTC
https://github.com/user-attachments/assets/ed88e34c-fed3-4b83-8a67-4716a9feeb7b
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-aito 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 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.
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
insightfaceandspeaker-recognitionbackends (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.cppprompt 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 blog.
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 |
|---|---|
| vllm.cpp | From-scratch C++20 port of vLLM for text generation: paged KV cache, continuous batching, prefix caching, safetensors + GGUF loading, engine-enforced structured output, on CPU, CUDA, Metal and Vulkan |
| 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 |
| magpie-tts.cpp | C++/GGML port of NVIDIA's Magpie TTS Multilingual 357M: 22.05 kHz mono text-to-speech in 5 voices and 9+ languages, with the NanoCodec neural codec and tokenizer/G2P embedded in a single GGUF |
| 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 | Mistral Voxtral-4B-TTS text-to-speech in pure C: 20 preset voices across 9 languages, 24 kHz WAV output, no dependencies beyond libc |
| 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 |
| trellis2.cpp | C++/GGML port of Microsoft TRELLIS.2: single-image to textured 3D mesh (GLB with PBR materials) |
| 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
- Blog: release write-ups, benchmarks and engineering notes
- 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}},
Sponsors
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:
Past sponsors
Individual sponsors
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!
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!