LocalAI: Run AI Models on Your Own Hardware

Overview

LocalAI is a Go-based server for running AI models on infrastructure you control. It brings models for text, vision, audio, image, and video generation behind a shared API, addressing the need for a self-hosted alternative to hosted AI services.

Its modular backend design lets users add model-specific engines as needed, rather than installing every runtime up front. It can run on CPU-only systems as well as supported GPU hardware, and includes API compatibility for OpenAI, Anthropic, and ElevenLabs interfaces.

Key Features

• Serves language, vision, audio, image, and video model tasks through a unified service.
• Provides compatible OpenAI, Anthropic, and ElevenLabs APIs.
• Uses separate backends that can be installed on demand.
• Supports CPU-only operation and NVIDIA, AMD, Intel, Apple Silicon, and Vulkan acceleration.
• Includes built-in agents with tool use, RAG, MCP, and skills.
• Offers authentication, quotas, and role-based access for multi-user deployments.
• Supports distributed inference and horizontal scaling.

Use Cases

• Developers can replace hosted model API calls with a service running on their own machines or servers.
• Teams can expose several model types through one API while installing only the required backends.
• Privacy-conscious organizations can process prompts and files within their own infrastructure.
• Operators can serve models across available CPU and GPU hardware, including distributed deployments.
• Users building agent workflows can use the built-in agents, retrieval, and MCP support alongside local models.

Getting Started

Run a CPU-only container and expose the service on port 8080:

docker run -ti --name local-ai -p 8080:8080 localai/localai:latest

For GPU images, model loading, and other installation options, see the repository README and documentation.

Alternatives

• freellmapi: FreeLLMAPI routes requests across hosted provider accounts and compatible endpoints, while LocalAI primarily serves models on local or distributed hardware.

Considerations

• Model performance depends on the selected model and available hardware. CPU-only use is supported, but acceleration is available for supported devices.
• Backends and model files are distinct from the core service, so choosing and obtaining suitable models is part of setup.
• The project covers many model types and backend engines, which can make configuration and compatibility selection more involved than using a single-purpose runtime.
• The repository reports 177 open issues, so users should check current documentation and issue discussions for backend-specific caveats.