workflowstacks
mcp-server

LocalAI: Deploy Any AI Model Locally—No GPU Needed

Run LLMs, vision, audio, or video models on any hardware without a GPU. Perfect for founders building AI products on a budget, backed by 49k+ GitHub stars.
49,455 stars+86★ this week4,492 forksGoGuide quality 9/10 · reviewed Sep 29Updated 10/10/2026100% 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.

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
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
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/localai && curl -fsSL https://workflowstacks.com/api/skills/localai/claude-skill -o ~/.claude/skills/localai/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How LocalAI: Deploy Any AI Model Locally—No GPU Needed works
Codeflow
Free to inspect

LocalAI: Deploy Any AI Model Locally—No GPU Needed 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
Go 69%JavaScript 11%C++ 5%Python 5%HTML 4%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    cmd/launcher/main.go
    Where the program starts running
  4. 4
    go.mod
    Dependencies and the commands it exposes
  5. 5
    core/cli/cli.go
    Inside core/ — the main logic begins here
What's in each folder
core/Core code — the actual logic1637 files
pkg/Core code — the actual logic291 files
internal/Core code — the actual logic3 files
backend/Backend / API1017 files
cmd/Command-line entry points12 files
docs/Documentation183 files
examples/Examples you can copy3 files
gallery/Folder84 files
READMEHas testsDocumentedCI checksDocker readyExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
mudler
Language
Go
Category
mcp-server
Published
3/18/2023

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Featured on WorkflowStacks
[![Featured on WorkflowStacks](https://workflowstacks.com/api/badge/localai.svg)](https://workflowstacks.com/skills/localai?utm_source=github&utm_medium=badge)
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