---
title: "Backends"
description: "Learn how to use, manage, and develop backends in LocalAI"
weight: 80
url: "/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`](https://github.com/mudler/LocalAI/blob/master/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`:

```bash
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:

```yaml
- 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:

```yaml
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

The default backend gallery tries `https://index.localai.io/backends`, then `github:mudler/LocalAI/backend/index.yaml@master`, then `oci://quay.io/go-skynet/local-ai-backends:gallery-backends`. The OCI fallback is signed by `gallery_publish.yml`. Its `artifact_verification` policy applies only to the gallery artifact; `verification` continues to control backend image signatures. Existing custom gallery lists are not changed. See [gallery publishing]({{% relref "features/model-gallery#official-gallery-publishing" %}}) for details.

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:

```bash
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:

```json
{
  "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:

```json
{
  "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:

```bash
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`:

```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](#backend-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:
   ```bash
   docker build -t quay.io/username/my-backend:latest .
   ```

2. Push to a container registry:
   ```bash
   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 %}}