LocalAI/docs/content/features/backends.md

title, description, weight, url

title	description	weight	url
Backends	Learn how to use, manage, and develop backends in LocalAI	80	/backends/

LocalAI supports a variety of backends that can be used to run different types of AI models. There are core Backends which are included, and there are containerized applications that provide the runtime environment for specific model types, such as LLMs, diffusion models, or text-to-speech models.

Available Backends

LocalAI ships 60+ backends covering text generation, speech-to-text, text-to-speech, music and sound generation, image and video generation, vision and object detection, audio processing, reranking, fine-tuning, and more. Each one is published as an on-demand OCI image with the appropriate acceleration variants (CPU, CUDA 12/13, ROCm, Intel SYCL, Vulkan, Metal, Jetson L4T).

For the complete list of backends, the model families they support, and their acceleration targets, see the [Backend & Model Compatibility Table]({{%relref "reference/compatibility-table" %}}). The authoritative source is backend/index.yaml, and the same catalog is browsable in the web UI under the Backends section.

Managing Backends in the UI

The Operate → Backends page is the canonical home for the complete backend lifecycle:

1. Catalog browses configured galleries, searches by name or description, filters by capability, and installs a backend. Catalog is the default view.
2. Installed shows the runtimes present on the host or cluster. Search and filter by user, system, update, or offline-node state, then select a backend to inspect its version, source, node placement, and lifecycle actions.
3. Variant and development builds remain opt-in refinements. Target-node links compose with the current view and selection instead of opening a separate management page.

The current view, search, filter, selected backend, and target node are stored in the URL. Browser Back and shared links therefore restore the same state.

Installs run in the background. The strip at the top of the app follows the current one, and Operate → Activity lists everything in flight, what needs attention, and what has finished, and is where a running install is cancelled or a failed one retried. See [Activity]({{% relref "operations/activity" %}}).

Each selected backend displays:

• Backend name and description
• Type of models it supports
• Installation status
• Install, reinstall, upgrade, or delete actions as appropriate
• Version, source, digest, placement, and catalog information

Backend Galleries

Backend galleries are repositories that contain backend definitions. They work similarly to model galleries but are specifically for backends.

Adding a Backend Gallery

You can add backend galleries by specifying the Environment Variable LOCALAI_BACKEND_GALLERIES:

export LOCALAI_BACKEND_GALLERIES='[{"name":"my-gallery","url":"https://raw.githubusercontent.com/username/repo/main/backends"}]'

The URL needs to point to a valid yaml file, for example:

- name: "test-backend"
  uri: "quay.io/image/tests:localai-backend-test"
  alias: "foo-backend"

Where URI is the path to an OCI container image.

To use a backend gallery or backend images that need authentication, such as a private registry, add a matching entry to the credentials file. See [Private Registries and Galleries]({{% relref "advanced/private-sources" %}}).

Backend Gallery Structure

A backend gallery is a collection of YAML files, each defining a backend. Here's an example structure:

name: "llm-backend"
description: "A backend for running LLM models"
uri: "quay.io/username/llm-backend:latest"
alias: "llm"
tags:
  - "llm"
  - "text-generation"

Verifying OCI Backends

Backend galleries can require keyless Sigstore signatures for every OCI image they provide. Add a verification policy to the gallery configuration, then enable strict integrity mode:

export LOCALAI_BACKEND_GALLERIES='[{"name":"localai","url":"https://index.localai.io/backends","mirrors":["github:mudler/LocalAI/backend/index.yaml@master"],"verification":{"issuer":"https://token.actions.githubusercontent.com","identity_regex":"^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/(heads/master|tags/.+)$"}}]'
export LOCALAI_REQUIRE_BACKEND_INTEGRITY=1
local-ai run

The policy pins the Fulcio issuer and the GitHub Actions workflow identity that signed the image. The identity expression covers development images produced from master and release images produced from tags. Use a narrower expression if your deployment only accepts one release channel.

Without strict mode, an OCI gallery without a verification policy installs with a warning. With strict mode, LocalAI refuses galleries without a policy, images without a compatible Sigstore bundle, and signatures that do not match the configured identity. Existing images published before bundle signing was enabled must be rebuilt or re-signed before strict deployments can install them.

An optional not_before RFC3339 value revokes signatures logged before that time. Advance it after a signing-workflow compromise, then rebuild or re-sign the trusted images:

{
  "verification": {
    "issuer": "https://token.actions.githubusercontent.com",
    "identity_regex": "^https://github\\.com/mudler/LocalAI/\\.github/workflows/backend_merge\\.yml@refs/(heads/master|tags/.+)$",
    "not_before": "2026-08-05T00:00:00Z"
  }
}

When one reusable workflow signs images for several repositories, the certificate identity names the shared workflow, not the repository that called it, so an identity match alone accepts an image signed for any of those repositories. Add source_repository to pin the repository the signature was made for. LocalAI compares it exactly with the source-repository extension of the signing certificate: a trailing slash, a different letter case or a .git suffix does not match. The value must be an https:// URL, or LocalAI refuses the policy when it uses it, when it installs a backend or fetches an oci:// gallery. LocalAI versions before this field existed ignore it and do not pin the repository, so upgrade every node, workers included, before you rely on it:

{
  "verification": {
    "issuer": "https://token.actions.githubusercontent.com",
    "identity_regex": "^https://github\\.com/example/signer/\\.github/workflows/release\\.yml@refs/tags/v.+$",
    "source_repository": "https://github.com/acme/backends"
  }
}

Pre-installing Backends

You can pre-install backends when starting LocalAI using the LOCALAI_EXTERNAL_BACKENDS environment variable:

export LOCALAI_EXTERNAL_BACKENDS="llm-backend,diffusion-backend"
local-ai run

Backend Directory Format

Every backend, whether the gallery installed it into the user-managed location or a system package shipped it, is a directory with one required file:

• run.sh — the entry point LocalAI executes to start the backend.

and one optional file, metadata.json:

{
  "name": "rocm-audio-cpp",
  "alias": "audio-cpp"
}

• name — the concrete backend name (defaults to the directory name).
• alias — registers this directory as a variant of a backend family. When several installed variants share an alias (cpu-audio-cpp, rocm-audio-cpp, ... all aliased to audio-cpp), a model config using backend: audio-cpp resolves to the variant best matching the host's capability (CUDA before Vulkan before CPU on an NVIDIA host, ROCm first on AMD, and so on). Each variant also stays individually addressable by its concrete name, e.g. backend: cpu-audio-cpp to keep VRAM free for other models.
• meta_backend_for — points a meta entry at a concrete backend directory installed next to it.

A directory without metadata.json is a plain backend under its directory name. Gallery installs write this metadata automatically (with additional bookkeeping fields such as gallery_url and installed_at); it only needs writing by hand when packaging backends outside the gallery.

System-Provided Backends

Backends do not have to come from the gallery: directories under LOCALAI_BACKENDS_SYSTEM_PATH (default /var/lib/local-ai/backends) are discovered on every scan, using the same directory format as user-managed backends. This is the integration point for distribution packages — the package manager installs backends there, while gallery installs keep living in the user-managed LOCALAI_BACKENDS_PATH.

One difference in error handling: a system directory with unreadable metadata is skipped with a warning, while unreadable metadata in the user-managed location fails the listing — a system package must never be able to break the discovery of the user's own backends.

Precedence between the two locations

User-managed backends always win over system-provided ones:

• Same name in both locations — the user-managed backend hides the system one entirely.
• Family takeover — installing any variant of an alias family into the user-managed location (e.g. from the gallery) replaces the whole system family: the alias resolves only among user-managed variants, and the system family's concrete names disappear from the listing. Variants of one family are versioned together; resolution never mixes installations of different origins within a family, and a stale system variant is not kept reachable.
• Names never get hijacked — a system variant's alias cannot take over a name that exists as a user-managed backend (including a meta backend): backend: audio-cpp keeps running the user's audio-cpp installation even if a system package later ships variants aliased to that name.

Creating a Backend

To create a new backend, you need to:

1. Create a container image that implements the LocalAI backend interface
2. Define a backend YAML file
3. Publish your backend to a container registry

Backend Container Requirements

Your backend container should:

1. Implement the LocalAI backend interface (gRPC or HTTP)
2. Handle model loading and inference
3. Support the required model types
4. Include necessary dependencies. Python backends are unpacked from the builder path into a runtime directory, so packages must be installed into the backend virtualenv with a regular pip install . / uv pip install . — not an editable (-e) source install. An editable finder keeps pointing at the vanished builder tree, and import fails after relocation.
5. Have a top level run.sh file that will be used to run the backend
6. Pushed to a registry so can be used in a gallery

{{% notice warning %}} An already-installed Python backend that was built with an editable install (for example vllm-omni from v4.0.0) keeps that broken finder until it is replaced with a rebuilt artifact. Reusing or renaming the unpacked directory does not rewrite the stale path; delete or upgrade the backend so the new site-packages copy is what runs. {{% /notice %}}

Getting started

For getting started, see the available backends in LocalAI here: https://github.com/mudler/LocalAI/tree/master/backend .

• For Python based backends there is a template that can be used as starting point: https://github.com/mudler/LocalAI/tree/master/backend/python/common/template .
• For Golang based backends, you can see the piper backend as an example: https://github.com/mudler/LocalAI/tree/master/backend/go/piper
• For C++ based backends, you can see the llama-cpp backend as an example: https://github.com/mudler/LocalAI/tree/master/backend/cpp/llama-cpp

Publishing Your Backend

1. Build your container image:

   docker build -t quay.io/username/my-backend:latest .
   

2. Push to a container registry:

   docker push quay.io/username/my-backend:latest
   

3. Add your backend to a gallery:

   • Create a YAML entry in your gallery repository
   • Include the backend definition
   • Make the gallery accessible via HTTP/HTTPS

Backend Types

LocalAI supports various types of backends:

• LLM Backends: For running language models (e.g., llama.cpp, vLLM, vllm.cpp, SGLang, transformers, MLX, and [RKLLM on Rockchip NPUs]({{% relref "features/rkllm" %}}) through the cloud-proxy backend)
• Speech-to-Text Backends: For transcription, forced alignment and speaker diarization (e.g., whisper.cpp, parakeet.cpp, moss-transcribe.cpp, [NeMo-Speech.cpp]({{%relref "features/nemo-speech-cpp" %}}), faster-whisper, [Whisper-Medusa]({{%relref "features/whisper-medusa" %}}), FunASR/SenseVoice, NeMo, [audio.cpp]({{%relref "features/audio-cpp" %}}))
• Text-to-Speech Backends: For speech synthesis (e.g., piper, Kokoro, VibeVoice, Qwen3-TTS, [NeMo-Speech.cpp]({{%relref "features/nemo-speech-cpp" %}}), [audio.cpp]({{%relref "features/audio-cpp" %}}))
• Sound Generation Backends: For music and audio generation (e.g., ACE-Step, [audio.cpp]({{%relref "features/audio-cpp" %}}))
• Sound Classification Backends: For sound-event classification / audio tagging - identifying everyday sounds like baby cry, glass breaking, alarms (e.g., ced.cpp)
• Image & Video Generation Backends: For diffusion and audio-conditioned avatar models (e.g., stable-diffusion.cpp, diffusers, vLLM-Omni, [MLX-Video on Apple Silicon]({{%relref "features/video-generation" %}}), [LongCat-Video]({{%relref "features/video-generation" %}}), [vllm.cpp / MiniMax-H3]({{%relref "features/video-generation" %}}))
• 3D Generation Backends: For image-to-3D mesh generation ([trellis2.cpp]({{%relref "features/3d-generation" %}}) — Microsoft TRELLIS.2, producing GLB assets with PBR textures)
• Vision & Detection Backends: For object detection, segmentation, depth, and face/voice recognition (e.g., rf-detr.cpp, locate-anything.cpp, sam3.cpp, insightface)
• Audio Processing Backends: For voice activity detection and audio enhancement (e.g., Silero VAD, LocalVQE, [audio.cpp]({{%relref "features/audio-cpp" %}}))
• Source Separation & Voice Conversion Backends: For splitting a mix into named stems (vocals, drums, bass) and for converting speech or singing to a target voice (e.g., [audio.cpp]({{%relref "features/audio-cpp" %}}))
• Utility Backends: For reranking, PII/NER token classification, fine-tuning, quantization, and vector storage (e.g., rerankers, privacy-filter.cpp, TRL, local-store, valkey-store)

See the [Backend & Model Compatibility Table]({{%relref "reference/compatibility-table" %}}) for the full catalog.

DS4 request cancellation

The DS4 backend stops inference when a client cancels or disconnects, including when a streaming response can no longer be written. Already-streamed chunks cannot be retracted; DS4 does not flush incomplete buffered parser state or persist an abandoned request to the disk KV cache. Cancellation is cooperative: DS4 checks it at safe prompt-prefill and decode-loop boundaries, so a GPU kernel already in flight may finish before the request stops.

llama.cpp request cancellation

The llama.cpp backend stops a streaming generation as soon as the response can no longer be written to the client, not only when the RPC is formally cancelled. A stream never recovers once a write fails, so the backend treats the first failed write as final and returns, which releases the slot the generation held.

This matters most for a model configured without a generation cap. With max_tokens: 0 and a large context_size, an abandoned request that keeps decoding occupies its slot until it reaches the context limit — tens of minutes on a large model — and every other request for that model queues behind it. A couple of abandoned requests is enough to make a healthy node look wedged.

Cancellation is cooperative and checked between decoded results, so a batch already in flight may finish before the request stops.

{{% notice tip %}} A generation cap is still worth setting. Cancellation only helps once a client has actually gone away; a client that waits receives the full context worth of tokens. Set max_tokens on the model config, and keep repeat_penalty above 1 so a repetition loop terminates on its own. {{% /notice %}}