mcp-server

LocalAI: AI on any hardware

Name: LocalAI: AI on any hardware
Author: mudler

Run AI models on any device with LocalAI, for founders who need flexible AI solutions.

48,567 stars4,367 forksGoHealth Score 9/10Updated 8/19/2026100% free · open source

What it does

LocalAI allows founders to run various AI models, including large language models, vision, voice, image, and video models, on any device without requiring a GPU.

Install / run

go get -u github.com/mudler/LocalAI

When to use it

•When you need to deploy AI models on devices with limited hardware capabilities
•When you want to test and develop AI models locally before deploying them to the cloud
•When you require a flexible AI solution that can run on different types of hardware

Quick start

1Clone the LocalAI repository using the command `git clone https://github.com/mudler/LocalAI.git`
2Build the LocalAI binary using the command `go build -o localai main.go`
3Run the LocalAI server using the command `./localai server`
4Use the LocalAI client to interact with the server, for example, `./localai client --model llm --input 'Your input text'`
5Refer to the `config.json` file to configure the LocalAI server and client settings

Ready-to-paste prompt

./localai client --model llm --input 'Write a product description for a new smartwatch'

Heads up: Make sure you have Go (version 1.17 or later) installed on your system, as LocalAI is built using the Go programming language and requires it to run

Saves to your device

Topics

agents

api

audio-generation

decentralized

distributed

image-generation

libp2p

llama

llm

mamba

mcp

musicgen

object-detection

rerank

stable-diffusion

text-generation

tts

How LocalAI: AI on any hardware works

Codeflow

Free to inspect

LocalAI: AI on any hardware is a very large Go project (~443k lines across 2317 code files, plus 134 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, MIT license, has a test suite.

Size

Very large codebase

~443k lines · 2317 code files · days to read — use, don't read

Setup

Developer setup

A real software project. Use it via its install path; don't expect to read it all.

Runs on

Go · Docker

No API keys detected