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 LocalAI web interface provides an intuitive way to manage your backends:

1. Navigate to the "Backends" section in the navigation menu
2. Browse available backends from configured galleries
3. Use the search bar to find specific backends by name, description, or type
4. Filter backends by type using the quick filter buttons (LLM, Diffusion, TTS, Whisper)
5. Install or delete backends with a single click
6. Monitor installation progress in real-time

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 backend card displays:

• Backend name and description
• Type of models it supports
• Installation status
• Action buttons (Install/Delete)
• Additional information via the info button

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.

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"
  }
}

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

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
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

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)
• 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, 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, [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.