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* feat(ui): rename the Install Models nav entry to Discover "Install Models" named the action rather than the destination, and it was the only multi-word entry in a rail of one-word ones (Home, Chat, Studio, Talk, Build, Operate). A bare "Models" was the obvious fix but it collides with the installed-models view under Host, which is a different page for a different job. "Discover" keeps the rhythm and says what the page is for. The icon moves from a download arrow to a compass for the same reason: the page is browsed before it is installed from. Translated in all seven locales rather than left to fall back, so a locale switch does not leave the entry in English. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): replace the gallery table with a rail and a detail pane The eight-column table was not the real problem; the click-to-expand row underneath it was. Variants, files and a VRAM estimate never fitted inside a <tr>, so they were pushed into a drawer that could hold one model at a time, could not be linked to, and had no room to say anything useful. The gallery is now a rail to scan and a pane that answers. The pane has two states and no third: with nothing selected it is the discovery page, and with a model selected it is that model's detail. Selection lives in the URL, so a model is linkable and Back steps out of the detail instead of off the page. The rail groups by capability while browsing and flattens to results the moment a term is typed. That is a rule rather than a toggle: once someone has said what they are looking for, the buckets are between them and the answer, and making the user choose would be handing them our problem. The detail pane plots VRAM against context length with the host's own limit drawn across it. This is new information, not a restyle. A single number invites "so will it run?", and the honest answer is usually "yes, up to a 32k context", which is a shape rather than a number. The estimates were already fetched for every context size, so it costs no new request. Backends that take no context length say so instead of being given a meaningless chart, and a host with no GPU gets no chart at all rather than bars with nothing to compare against. The split-button variant menu goes with the actions column. The pane lists every build with its backend, quantization, size, fit and a details disclosure, each installable, which is what the dropdown was a cramped substitute for. Its tests move onto that list; the three contracts it alone carried (fetch-once caching, the loading state, an unfit build staying installable) are backfilled against the pane. RecommendedModels moves inside the pane, where it has the width to argue for a model instead of listing one, and keeps its own dismissal and collapse. Rail entries carry no description. Two lines is the budget and the second is better spent on whether the thing will run; the stripped-Markdown contract moves to the pane's lede, tooltip included. e2e: 123 passing across models-gallery, navigation, recommended-panel, model-artifact-operation, operations-strip and page-render-smoke. Inline styles in Models.jsx drop from 82 to 41. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * refactor(ui): extract the split view into shared components Discover shipped its rail, pane and detail header as private functions inside Models.jsx. Backends and Host have the same defect and want the same shape, so leaving them there guarantees three rails that drift. SplitView, EntityRail, DetailHeader and StatGrid now live under components/split/. EntityRail is deliberately data-driven: a surface maps its own entity onto { id, name, icon, meta, stripe, groupId } and keeps its vocabulary to itself, which is what stops the rail learning about models, backends and loaded state all at once. The CSS moves with it. What was .discover__rail is .entity-rail, .discover__ pane is .split-view__pane and so on, because a class named after one page is a lie on the next two. Only what is genuinely Discover's stays behind the old prefix: the shelves, the hero and the VRAM-by-context chart. Two additions the shared rail needs and Discover did not: a state stripe, for surfaces read by condition before they are read by name, and an empty label. Discover passes neither. No behaviour change. e2e 100 passing across models-gallery, navigation and models-recommended-panel. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): put the backend gallery on the split view Same defect as the model gallery, so the same shape: a seven-column table over a click-to-expand row that was the only place the repository, licence, tags and links could go. The rail groups backends by the use case they serve, sharing Discover's taxonomy on purpose: a backend is the runtime a use case needs, so "vision" ought to mean the same thing one level down. It flattens on a query for the same reason it does on Discover. The zero state is the one real departure. A backend's fitness is not free memory, it is the accelerator and platform it was built for, so the pane leads with what this host is, then what is not installed yet, then whether anything installed has gone stale. The table listed 37 runtimes and left "which of these can even run here" entirely to the reader. Distribution moves into the pane, which is the one thing a row could never carry: which nodes hold a copy and which do not, with the install-on-more control next to it rather than squeezed against a chip. The distributed and target-node action logic is unchanged, including the guard that keeps a hardware-specific build off the fan-out path. The split-button popover loses its per-row anchoring because there are no rows; one pane, one anchor. Selection lives in ?backend=, preserving the ?target= scope rather than clobbering it. e2e: 139 passing across models-gallery, navigation, backends-management, models-recommended-panel, nodes-per-node-backend-actions, page-render-smoke, operations-strip and model-artifact-operation. The backends spec gains six split-view tests; its three description-cell tests move onto the pane lede. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): put the Host inventory on the split view The last of the three surfaces, and the one that is not a catalog. Both tabs had the same click-to-expand row, so the shell transfers; what does not transfer is the zero state, because there is nothing to discover in your own inventory. With nothing selected the pane reports what is happening: how many models are loaded, what failed, what has an update, and which models are holding VRAM right now. Every number was already on the page. None of them had been assembled into one statement, so "what is going on" was a question the tabs could not answer however long you looked at them. The rail buckets by state rather than capability - Running, Idle, Disabled for models; Update available, Installed for backends - which is the opposite of the galleries and deliberately so: nobody opens Host wondering which of their models does vision. Entries carry a state stripe for the same reason. Load and Stop are promoted out of the kebab, because that is what an operator came for; the rest stays behind the menu rather than diluting it. Adopted, pinned and alias badges follow the model into the pane: they are facts about the thing, not about its state, and the rail line is spent on state. Deliberately NOT done: folding the two tabs into one rail, as the mock had it. It costs five URL parameters, the manage-tab localStorage key and the stat-card shortcuts, all of which are live deep-links today. The tabs stay as the group selector; merging them is a follow-up with its own migration. e2e: full suite 355 passing. New host-split-view spec; alias-template, manage-logs-link, manage-action-menu-position and model-editor-back-nav move off `.table` and the row kebab onto the rail and the pane. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * polish(ui): accessibility and consistency pass over the three split views Findings from a pass over what the previous four commits actually shipped, rather than what they were supposed to. The rail was not a listbox. ARIA lets a listbox contain options and groups, and nothing else, but each group's collapse control is a button that has to sit inside the scroller with the entries it folds. It is now a labelled group of buttons, which is the honest description; selection is announced with aria-current and the arrow keys are unaffected. Every entry was its own tab stop, so tabbing past a forty-entry rail to reach the pane took forty keystrokes. Roving tabindex makes the rail one stop, and arrowing now moves focus with the selection instead of leaving it behind on an entry Tab can no longer reach. The rail rounds its corners with overflow:hidden, which was clipping the focus ring off the first and last entries entirely. Inset outlines fix it. A 30px row is fine under a mouse and too small under a thumb, so coarse pointers get a 44px target without costing density on a desktop. One slot said three different things: "9 models loaded" on Discover, "12 loaded" on Backends, "3 of 9" on Host. All three lists are a page of a larger set, so all three now say so the same way. Also removed: an emptyLabel prop on EntityRail that nothing passed, its dead CSS rule, and MODELS_COLSPAN and ResourceRowDesc, which died with the tables. e2e: full suite 355 passing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): correct three defects only a real gallery exposed Running the branch against a live instance with 1,595 models and 1,017 backends, rather than against mocked fixtures, surfaced three things the e2e suite could not. Grouping did nothing. The rails matched on the use-case keys the filter chips send (`chat`, `tts`, `transcript`), but those are a server-side vocabulary the handler maps onto entries. What entries actually carry is free-form and inconsistent: models come back tagged `llm`, `gguf`, `vision`, `coding`, and backends `LLM`, `text-to-text`, `audio-transcription`. Nothing matched, so every model landed in "Everything else" and the feature was decorative. Grouping now lives in utils/entityGroups.js, shared by both galleries, matching case-insensitively against the vocabulary the API really uses, with the entry's backend as a fallback signal - a backend named `whisper` is a speech backend whatever its tags say. Order is specific before general and that is load-bearing: a vision model is tagged `llm` too, so testing text first would swallow it. The zero state claimed GPU memory on a machine with no GPU. The resources endpoint reports system RAM in the same field when gpu_count is 0, so the hero read "84.4 GB of GPU memory" next to the recommendations panel correctly saying "No GPU detected". The number was never wrong, only its label; it now says system memory unless a GPU is actually present. The page title still said "Install Models" under a nav entry saying Discover. Also: the keyboard test named the model it expected to arrive at, which made it a hostage of the grouping table and broke the moment the buckets were fixed. It now asserts that the selection moves and returns. e2e: full suite 355 passing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): the filters and the rail were fighting over the same job Four things you find odd on Discover, and they turn out to be one mistake seen from four sides. The rail grouped the current page. The listing is paginated at nine rows, so those bucket headers described nine entries out of 1,595, and turning a page reshuffled the sections under the reader. The structure was never stable because it was computed over the wrong set. The chips were redundant for the same reason, seen from the other side. They send tag= and filter all 1,595 server-side. The rail grouped nine of them client-side by the same axis. Two controls for one job, and the weaker one was the one this branch added, so it goes. Grouping stays only on Host, where the list is complete, local, and bucketed by state rather than capability. The search bar felt odd because it sat in a full-width band while the thing it narrowed was a 290px rail below and to the left. The whole band now lives in the rail column: search, backend, use cases, refinements, then the list it narrows. One column to say what you want, one to show what you got. Nineteen chips do not fit at that width, so they fold into a disclosure that states the selection. A disclosure and not a popover, deliberately: picking use cases is multi-select and interleaves with the backend select and the toggles below, and a popover dismisses itself the moment you touch either. The header held two counts and two buttons at arm's length from all of it. The counts were the third statement of the same number on one screen, after the rail's "9 of 1,247" and the pane's own headline, so they go. The buttons move into the pane's zero state, which is the surface that answers "what do I do here". Also: the two first-run empty states wore .loading-center, which is display:flex in the default row direction because it exists to centre one spinner. With four children that put the icon, the heading, the sentence and the buttons on a single line with no gap. They are now a proper full-height empty state. e2e: full suite 353 passing. Grouping tests are replaced by ones asserting the rail stays flat; chip tests open the disclosure first; two filter-layout tests that asserted the old three-band arrangement now assert the column. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * polish(ui): make Discover a full-height view, group the chips, name the refinements Four things, all of them the same complaint: the page read as a document with controls scattered on it rather than as one view. The header is fused. A title block with its own padding, a subtitle and two counts made the split view look like an attachment to a document that happened to sit below it. It is now a slim bar carrying the title, the count and the two page-level actions, and the split fills the rest of the window. Rail and pane scroll independently, so the filters and the pane's headline stay put while a long list moves under them. The chips group. Nineteen in a flat row is a lot to scan even behind a disclosure, and they already belong to the four families the rest of the UI speaks, so they are bucketed by those. "All" sits on its own above them without a heading, because it is a reset rather than a use case. The refinements stop looking dumped. When the band became a column they were three controls left where they landed; they now read as a named section with one control per row. The zero state suggests again. It had decayed into a "Browsing / 9 of 1,247 / select a model" line that restated the count for the third time on one screen. It now offers the four use cases as tiles that set the filter, which is the shelf idea from the mock without inventing curation or paying for a second fetch. Two bugs found by looking at it rather than at the tests: the disclosure was clamped to 190px, which cut it off partway through its third section so two of the five never appeared at all; and the creation actions rendered twice, once in the new bar and once in the pane hero a few pixels away. e2e: full suite 353 passing. The chip-row test now holds its contract across the per-family rows rather than a single one, and additionally asserts every family is present and non-empty. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): pin the split view's height so a long detail scrolls the pane Selecting a model with a long description grew the whole page and dragged the rail down with it, which is the opposite of what "full height" was supposed to buy. The flex chain was right and the ceiling was missing. .app-layout and .main-content are min-height:100dvh, which is a floor: flex distributes free space but nothing caps growth, so a pane taller than the viewport expanded the column, the document scrolled, and the rail stretched to match. height:100% on the pane then resolved against an auto-height parent and did nothing. The chat route already solves this by pinning .main-content to 100dvh. The same treatment now applies to any route containing a .page--app, selected with :has() so the shell does not have to learn which pages happen to be split views. Below the stacking breakpoint the pin is lifted, because two stacked halves in two short scrollers is worse than a page that scrolls. Measured on a live instance: document height stays at the viewport across selection (950px either side) and the pane overflows internally instead. Adds discover-height.spec.js, which asserts the page height and the rail height are unchanged by selection and that the pane is the thing that scrolls. The existing specs could not have caught this: they mock short descriptions, and the bug only appears when the pane has more content than the viewport holds. e2e: full suite 355 passing. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * feat(ui): give Backends and Host the full-height view, and fix the Update button Backends now matches Discover: the header fuses into a slim bar carrying the title, the count and the page-level actions, the filters move into the rail column where they narrow the rail and nothing else, and the split fills the window. Its seven chips fit at rail width, so unlike Discover's nineteen they need no disclosure. Host gets the bar and the height; its resource monitor, summary cards and tabs stay above the split, because those are read once while the rail and the pane are worked in. Two things the height change surfaced. The console layout is a flex row with align-items:flex-start, so its body sizes to content. Right for the pages it was built for, wrong for a split view, which needs a ceiling to scroll inside: without it the Backends rail ran past the viewport and over the footer. Pinned with :has() so only split-view routes are affected. The filters vanished when nothing matched. Both galleries swapped the whole shell for an empty state, which took the search box and the chips with it, so the page said "try adjusting your search or filters" while offering neither. The shell now stays and the empty state moves into the pane. Also fixes the Update control on Host, which had no className at all and rendered as bare text, next to a status span that had picked up btn classes and two copies of `fas` and so rendered as a button you cannot press. They have swapped appearances back. e2e: full suite 355 passing. The render-smoke selector learns .view-bar__title, since the pages it checks no longer all use PageHeader. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * fix(ui): keep the view mounted while searching, and bring rail grouping back Searching replaced the whole view with a loader. The search box lives in the rail column, so every debounced refetch unmounted the field being typed into and dropped its focus with it. The list, the filters and the pane went too. The shell now stays and the rail says it is busy: a sweep bar under its header and the stale list dimmed, so the eye knows the answer is being replaced without losing its place. A cold start still gets the skeleton, because there is nothing to keep. The condition for that is "nothing has loaded yet", not "the list is empty". Those differ exactly when someone is editing a query that matched nothing, and getting it wrong there would unmount the view on the keystroke after a no-results search - the worst possible moment. Grouping comes back on both galleries. It was removed because nine rows could not fill five buckets, so a page turn rebuilt the rail's whole structure. That was a symptom of the page size rather than of grouping: the rail now asks for 30 rows instead of 9 (Backends 60 instead of 21), which is enough for the sections to read as structure and turns five times fewer pages. The order of the sections is fixed, so what changes between pages is membership, not arrangement. Grouped while browsing, flat while searching, as before: once a term is typed the buckets stand between the reader and the answer. Also gives GalleryLoader a class and a testid instead of six inline style declarations on a bare div, which is why nothing could select it. e2e: full suite 359 passing, including a new spec asserting the search box keeps its focus and its value across a refetch, and that a cold start still shows the skeleton. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * perf(gallery): stop invalidating the VRAM estimate caches on every request Searching or turning a page felt slow. It was not the search and not the listing: /api/models answers in 3-9ms. It was the VRAM estimate, which the gallery asks for once per row, and which took ~2.3s every single time however often the same model was asked about. pkg/vram already caches what makes that expensive - the remote content-length probes, the GGUF metadata reads and the HF repo sizes. Those caches key on a gallery generation counter, and AvailableGalleryModelsCached triggered a background refresh on every call, with each refresh bumping the counter. One page view is one listing request plus thirty estimate requests, each of which re-read the gallery and started another refresh, so the generation moved constantly and every cache entry was stale before it could ever be read. The caches were dead in production. Three changes, each doing one thing: A refresh interval. The cached list is still served immediately; this only decides how often re-fetching from upstream is worth starting. Five minutes, as a package variable so tests can drive it without waiting. A generation bump only when the gallery actually changed. An unchanged gallery re-fetched on schedule must not throw away work that is still valid, which is the difference between an estimate costing nothing and costing a network round trip. A separate "loaded" flag. The cache engaged on `cached != nil`, so a gallery that legitimately holds nothing read as never-loaded and took the blocking path on every call, bumping the generation each time. Found by the test for the interval, which could not pass while this was true. Measured against a live instance with 1,595 models: one estimate, repeated 2.3s -> 2ms a page of 30, in parallel 10s -> 0.04s A first, genuinely unseen model still costs its remote probe. That is inherent; what changed is that it is now paid once per model per gallery version rather than once per request. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * perf(gallery): warm VRAM estimates at startup, and stop the UI waiting on them Two halves of the same complaint: the gallery stalls on VRAM estimation. Server side, the estimates are now warmed in the background at startup. Estimating an entry nobody has asked about costs a remote probe of its weight files, and the gallery needs one per row, so the first visitor was paying for the whole page. The warm-up walks the gallery in the order the UI lists it, so the first page is ready before anyone reaches it. It is bounded and it never blocks: 300 entries at 4 at a time by default, on its own goroutine, stopping with the server's context. Warming the whole gallery would be thousands of probes on every boot, which is rude to the upstream and slow to finish; warming nothing leaves the first page paying two seconds a row. Anything past the limit still warms itself on first view. LOCALAI_VRAM_WARM_LIMIT=0 turns it off for an air-gapped host, LOCALAI_VRAM_WARM_CONCURRENCY=1 slows it for a metered link. Client side, the page no longer waits on estimates it does not need yet. It fired one request per row at once; a browser allows about six connections per host, so thirty estimates took every slot and the request behind a click - the variant list, an install - queued behind work nobody asked for. That is the freeze: the list was already usable, and the UI was busy fetching sizes. Four at a time leaves room for the interactive request to overtake, and a row whose estimate is still in flight says "sizing…" rather than leaving a blank where a number will appear. buildEstimateInput moves to core/gallery as EstimateInput, since the handler and the warmer both need it. Measured against 1,595 models, from a cold boot: page 1, 30 estimates in parallel 10s -> 0.04s full warm-up (299 of 300 entries) 3m, in the background Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] * chore: untrack data/.local_user_id and ignore the runtime data dir `local-ai run` writes its instance state under ./data when started from the repo root, which is exactly what a contributor testing a build does. The identity file ended up committed on this branch by a `git add -A` while verifying the gallery changes against a live instance. Anchored, so it matches the runtime directory at the repo root and not a `data` directory nested inside some package. Signed-off-by: Ettore Di Giacinto <mudler@localai.io> Assisted-by: Claude:claude-opus-5 [Claude Code] --------- 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 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 |
|---|---|
| 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
- 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}},
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!