// local-llm · janhq/jan

Jan

Jan is an open source alternative to ChatGPT that runs 100% offline on your computer

★ 44.8k stars⑂ 3.1k forksCustom (check repo)updated 2026-10
▦ visual — this project's README ships no embeddable screenshot,
so here's a placeholder. The guide below is complete regardless.
// project facts

Project facts

Jan is an open-source local llm project by janhq (janhq/jan on GitHub). It has 44,774 GitHub stars and 3,061 forks, is released under the Custom (check repo) license, and was last updated on GitHub on 2 October 2026.

Repository
janhq/jan
GitHub stars
44,774
Forks
3,061
License
Custom (check repo)
Last updated on GitHub
2 October 2026
Category
Local LLM
Listing
Full install guide
Official site
Official site
// how it works

How it works

  1. Install the runtime

    One-command installers exist for every major OS; servers and engines use Docker or simple binaries.

  2. Download a model

    Pull from a built-in library or point at any GGUF/compatible weights you already have.

  3. Chat or call the API

    Use the bundled chat UI, or point your apps and coding agents at the local OpenAI-style endpoint.

  4. Tune for your hardware

    Pick model sizes that fit your RAM/VRAM; quantization options trade a little quality for a lot of speed.

// features

Why people love it

Total privacy

Prompts never leave your machine — no accounts, no logging, no training on your data.

Works offline

Once weights are downloaded, everything runs with the network cable unplugged.

OpenAI-style APIs

Local endpoints your existing tools and code can call with minimal changes.

Huge model choice

Llama, Qwen, Gemma, DeepSeek, Mistral and more, in sizes from phone-friendly to workstation-class.

Agent integrations

Coding assistants and automation tools plug straight into the local server.

Quantization support

Run bigger models on smaller hardware via GGUF and friends.

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// run it

Run it yourself

  1. Step 1 — Get the code

    Clone the repository or download the latest release for your OS.

    git clone https://github.com/janhq/jan.git
  2. Step 2 — Follow the install guide

    Every project documents its own setup — the README's install section is the source of truth.

  3. Step 3 — Launch

    Start the app or server as documented and open the interface it provides.

// use cases

What you can build

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// faq

Questions, answered

How much RAM do I need?

Roughly the model size in GB plus headroom — 8B models fit comfortably on 16GB machines with quantization.

Local or cloud — which is better?

Local wins on privacy and cost; cloud wins on raw frontier quality. Many people use both.

Who maintains Jan?

It is an open-source project at janhq/jan with 3,061 forks. Check the repo's contributors and recent commits to see how active maintenance is.

Which model should I start with?

A recent mid-size open model in the library's recommendations — start small and scale up.

Can my apps use it?

Yes — the local API plus client libraries make it a popular backend for indie apps.

Does it run on Apple Silicon?

Yes — several of these are optimized for it, and unified memory helps a lot.

// keep exploring

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// alternatives

Alternatives in Local LLM

The most-starred other Local LLM projects in the LibreAI catalogue — the realistic shortlist if Jan is not the right fit.

Get Jan

Free and open — grab the source, star the repo, and start creating.