LocalAI
A drop-in local replacement for the OpenAI API, running on hardware you already own.
What it is
LocalAI is a self-contained inference server that runs language models, image generators, speech recognition and speech synthesis on ordinary hardware — including machines with no graphics card at all. It exposes an API shaped like the OpenAI one, so an application written against that service can often be pointed at your own machine by changing a single base URL.
It is written in Go and ships as a single binary or container, bundling the backends it needs instead of asking you to assemble a Python stack first. Models are described by small configuration files, which is more manual than a one-command downloader but also means you can pin exact versions, quantisation and parameters.
Choose it when you need a local endpoint that behaves like the hosted API across several kinds of model — text, image and audio — and you are willing to trade some convenience for control and CPU support. For the simplest local chat, a lighter runner is easier to live with.
Why we recommend it
The API is a drop-in for OpenAI
Change the base URL and most client libraries keep working. Existing code, SDKs and tools do not need rewriting to run against local models, which is the single biggest reason people pick it over a bespoke runtime.
It runs without a GPU
The project deliberately supports CPU inference. On a laptop or a small rented server with no graphics card, that is the difference between a local model being possible at all and not.
More than text
The same server handles image generation, speech recognition and speech synthesis, so one process can back several kinds of application instead of you running four separate stacks.
One binary, few dependencies
Because it is compiled and bundles its backends, there is no fragile Python environment to build or repair. You download it, point it at models and run it.
How it scores
These are our editorial ratings, not benchmark results. We score on how the project behaves in practice — how long it takes to get running, how good the documentation is, how responsive the maintainers are, and how well it holds up over time. The licence and star count above are read from GitHub and are a snapshot.
Pros and cons
Strengths
- OpenAI-compatible API, so existing clients work with a URL change
- Runs on CPU as well as GPU, which broadens the hardware it supports
- Single binary or container — no Python environment to maintain
- Covers text, image and audio models in one server
- Per-model config files give precise control over quantisation and parameters
Trade-offs
- Configuring models means editing YAML by hand rather than a one-line pull
- Getting a specific model to work well can take real trial and error
- The first model load downloads large backends, so disk and bandwidth matter
- Documentation has gaps and can lag behind the fastest-moving features
How to get it
Direct download
Clicking a button downloads the file straight from the project's release — you stay on this page.
One-line install
Prefer the terminal? Copy a command and you are done.
docker run -p 8080:8080 --name local-ai -ti localai/localai:latest-cpu
Source and documentation
At a glance
| Category | Local AI |
|---|---|
| Licence | MIT |
| Written in | Go |
| GitHub stars | 49,021 |
| Repository | mudler/LocalAI |
Licence and star count were read from the GitHub API when this page was generated and are a snapshot. Check the repository for the current figure and the authoritative licence text.
Other Local AI tools
We are not affiliated with LocalAI or its maintainers. We list it because it is genuinely useful and genuinely free. If you plan to build a commercial product on top of it, read the licence on the repository first.