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* ci: extract free-disk-space composite action Consolidate the apt-clean + dotnet/android/ghc/boost removal blocks from backend_build.yml, image_build.yml, and test.yml into a single composite action. The three callers had slightly different inline blocks; the composite uses the more aggressive backend_build/image_build variant for all three callers — test.yml jobs now also purge snapd, edge/firefox/ powershell/r-base-core, and sweep /opt/ghc + /usr/local/share/boost + $AGENT_TOOLSDIRECTORY. Idempotent and skipped on self-hosted runners. In test.yml, actions/checkout now runs before the composite action call because the composite lives at ./.github/actions/free-disk-space and requires a checked-out repo. The original ordering relied on jlumbroso/free-disk-space@main being a remote action; this is the minimum-invasive change to support a local composite. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: path-filter backend.yml master push Run scripts/changed-backends.js on master pushes too (not just PRs) so unrelated commits don't rebuild all ~210 backend container images. Tag pushes still build the full matrix via FORCE_ALL. Push events use the GitHub Compare API to diff event.before..event.after. Edge cases (first push with zero base, API truncation beyond 300 files, missing fields, network failure) fall back to "run everything" — better safe than silently miss a backend. The matrix literal moves from .github/workflows/backend.yml into a new data-only file at .github/backend-matrix.yml (outside workflows/ so actionlint doesn't try to parse it as a workflow). Both backend.yml and backend_pr.yml now consume the dynamic matrix output uniformly via fromJson(needs.generate-matrix.outputs.matrix); the script reads the matrix from the new location. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: bound max-parallel on backend-jobs matrices Cap to 8 concurrent jobs to avoid queue starvation on the shared GHA free pool while migration is in flight. Lift after Phases 4-5 retire the self-hosted runners. Also drops a leftover commented-out max-parallel line that lived in backend.yml since the previous matrix shape. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: scope backend cache per arch, push by digest Prepare backend_build.yml for the multi-arch split. The reusable workflow now accepts a `platform-tag` input ("amd64" / "arm64") that scopes the registry cache to cache<suffix>-<platform-tag> and (on push events) pushes the resulting image by canonical digest only. Digests are uploaded as artifacts named digests<suffix>-<platform-tag> for the merge job (Task 2.2) to consume. `platform-tag` is optional with empty default during the migration — existing callers continue to work unchanged (their cache key just becomes `cache<suffix>-`, an orphaned but valid key). Tasks 2.3+ will update callers to pass an explicit "amd64" / "arm64" value. Phase 6 flips the input to required: true once every caller is wired. PR builds keep their existing tag-based push to ci-tests but pick up the per-arch cache key. Multi-arch PR builds remain emulated in this commit; they migrate when the matrix entries split (Tasks 2.3+). Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: add backend_merge.yml reusable workflow Joins per-arch digest artifacts (uploaded by backend_build.yml when called with platform-tag) into a single tagged multi-arch manifest list via `docker buildx imagetools create`. Called once per backend by backend.yml after both per-arch build jobs succeed. The workflow generates final tags identically to the previous monolithic build job (same docker/metadata-action invocation), so consumers of quay.io/go-skynet/local-ai-backends and localai/localai-backends see no tag-shape change. Two imagetools calls (one per registry) reference the same per-arch digests under different image names. Not yet wired into backend.yml — Tasks 2.3+ rewrite individual matrix entries to expand into per-arch + merge jobs that call this workflow. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: relocate Docker data-root to /mnt on hosted runners GHA hosted ubuntu-latest runners ship a ~75 GB /mnt drive that's unused by default. Stopping Docker, rsync'ing /var/lib/docker to /mnt, and restarting with data-root pointing there yields ~100 GB of working space (combined with the apt-clean from Task 1.1) — enough for ROCm dev image + vLLM torch install + flash-attn intermediate layers. This is the structural change that lets Phases 4 and 5 of the migration plan move the bigger-runner and arc-runner-set jobs onto ubuntu-latest. The composite action is no-op on self-hosted runners (where /mnt isn't expected) and on non-X64 runners (Task 3.2 verifies the arm64 hosted pool's /mnt shape separately before enabling). Wired into both backend_build.yml and image_build.yml between free-disk-space and the first Docker operation. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci(setup-build-disk): chmod 1777 /mnt/docker-tmp buildx CLI runs as the unprivileged 'runner' user and creates config dirs under TMPDIR before binding them into the buildkit container. /mnt is root-owned by default, so the original mkdir produced a permission-denied when buildx tried to write there: ERROR: mkdir /mnt/docker-tmp/buildkitd-config2740457204: permission denied Mirror /tmp's permission mode (1777 — world-writable with sticky bit) on /mnt/docker-tmp so non-root processes can stage their config. Caught by the first PR run (image-build hipblas job) on PR #9726. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> * ci: weekly full-matrix rebuild via cron Path-filtering backend.yml master push (the previous commit's main optimization) skips backends whose source didn't change. That broke the DEPS_REFRESH cache-buster's coverage: the build-arg keyed on %Y-W%V busts the install layer's cache on a new ISO week, but only when the build actually runs. Untouched Python backends (torch, transformers, vllm with no version pin) would otherwise ship stale wheels indefinitely. Add a Sunday 06:00 UTC cron that fires the full matrix. Schedule events have no event.ref / event.before, so the script's changedFiles == null fallback (scripts/changed-backends.js) emits the full matrix automatically — no script change needed. C++/Go backends with pinned deps cache-hit and complete fast, so the weekly cost is dominated by Python re-resolves which is exactly what we want. workflow_dispatch added so a maintainer can trigger an ad-hoc full-matrix rebuild without faking a tag push. Assisted-by: Claude:claude-opus-4-7 Signed-off-by: Ettore Di Giacinto <mudler@localai.io> --------- Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Co-authored-by: Ettore Di Giacinto <mudler@localai.io>
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 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
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
- April 2026: Voice recognition, Face recognition, identification & liveness detection, Ollama API compatibility, Video generation in stable-diffusion.ggml, Backend versioning with auto-upgrade, Pin models & load-on-demand toggle, Universal model importer, new backends: sglang, ik-llama-cpp, TurboQuant, sam.cpp, Kokoros, qwen3tts.cpp, tinygrad multimodal
- 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.
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 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.
Resources
- Documentation
- LLM fine-tuning guide
- Build from source
- Kubernetes installation
- Integrations & community projects
- Installation video walkthrough
- Media & blog posts
- Examples
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:
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!