Jan

Jan is an open-source desktop app that runs language models entirely on your own hardware. It is positioned as a self-hosted alternative to ChatGPT and Claude: you download a model, chat with it, and no account or cloud service is required. Jan is built on llama.cpp/GGML and ships as a Tauri app (the Rust implementation is why the repository's primary language reads as Rust).

At a glance

Why it is on our list

• It is the gentlest on-ramp to "a ChatGPT on my laptop" for people who are not ready to touch a terminal.
• Apache-2.0 with a public repository: inspectable, forkable, repairable.
• It exposes a local OpenAI-compatible API server, so the same models can back other tools later.

Install and first run

1. Download the installer for your platform from https://jan.ai/download.
2. Install and launch. Jan does not require an account.
3. Pick a foundation model in the app, download it, and start chatting.

If you prefer written reference material over the in-app flow, start with the docs overview at https://docs.jan.ai/desktop.

Configuration worth knowing

• Local API server. Jan can expose an OpenAI-compatible endpoint on your machine; other local tools can then point at Jan instead of at a cloud provider.
• Data folder. Jan keeps models and settings in a data folder that you can relocate — useful when your system drive is small.
• Engine settings. Model parameters (context length, GPU layers, sampling) are adjustable per model; leave defaults until something actually misbehaves.

Common pitfalls

• Stale versions on Linux and older NVIDIA cards. The Jan 0.8.6 hotfix specifically addressed Linux AppImage model-loading crashes, NVIDIA Turing GPU compatibility, and Qwen3.8 speculative-decoding slowdowns. If you hit crashes or absurdly slow generation on that hardware, update before debugging anything else.
• Downloading a model that does not fit your VRAM. A failed or glacially slow load is usually a sizing problem, not a bug. Choose a smaller quantization first, prove the pipeline works, then scale up.
• Confusing "local" with "private from your network". Local inference keeps prompts on your machine; it does not stop a model from being served to other devices if you deliberately expose the API server.

Tips and tricks

• Start with one small model, confirm it responds at acceptable speed, then add larger ones. This separates "my hardware is too weak" from "my configuration is wrong".
• Once a model works in chat, turn on the local API server and point a second tool at it — that is the cheapest way to learn how local inference is reused across apps.
• Keep the app updated; the project ships fixes for specific GPU generations rather than generic "performance improvements".

Videos

All links below were resolved through YouTube's oEmbed API on 2026-10-28, so the titles and channels are the real ones.

• Run LLM Models Locally | Setup Jan.ai | Full Tutorial — Pyzit.
• How to Run AI Models Locally with Jan AI | Install Qwen & DeepSeek AI — AI2Play.
• How to Install Jan AI on Linux | Run Local AI Models Easily — World of Linux.
• Install Jan to Run LLM Offline and Local First — Fahd Mirza.

Related

• Unsloth Desktop — the current Owners Club recommendation for local inference plus a ChatGPT-like experience.
• Choosing local AI tools — which app fits which goal.
• The local-first AI stack — how the pieces fit.

Verification note: license, repository, and platform facts were read from the official site, the project docs, and the repository LICENSE file on 2026-10-28. Feature descriptions come from the project's own documentation and should be re-checked before being quoted as current.