Jan AI for Your Own Desktop Local LLM (Free & Open Source)

For developers and hobbyists who want full control over their data—and zero reliance on cloud APIs—Jan AI delivers. Built by Menlo Research and licensed under Apache-2.0, Jan AI makes it simple to download, manage, and run large language models entirely on your own hardware. Whether you’re handling sensitive documents in regulated industries or tinkering offline in remote locations, Jan AI brings the power of LLMs right to your desktop.

Core Benefits and Use Cases

1. Privacy-First Inference

All your prompts and context stay on-device. No third-party servers, no hidden telemetry—ideal for confidential workflows and regulated environments.

2. Offline Operation

Download a model once, then fire off queries without an internet connection. Perfect for air-gapped systems, field work, or anywhere connectivity is spotty.

3. Cost Control

Swap recurring cloud-inference fees for a one-time hardware investment. Great for teams experimenting frequently or hobbyists running dozens of tests.

4. Low Latency

By cutting out network round-trips, local inference delivers faster response times—essential for interactive desktop apps or real-time development feedback loops.

Key Features of Jan AI

Cross-Platform Installers

Prebuilt binaries for Windows, macOS, and Linux mean you can skip dependency hell. Choose from stable, beta, or nightly releases to match your appetite for new features versus battle-tested stability.

Hugging Face Integration

Browse, preview, and download dozens of models (e.g., Qwen, LLaMA, Mistral) directly from the UI. See parameter counts and file sizes up front so you can pick models that fit your hardware.

Local API Server

Spin up an HTTP endpoint on port 1337 and call it just like OpenAI’s API—yet every inference stays on your machine. Built-in CORS support makes it a breeze to integrate with web front-ends or local scripts.

Example: Install and start Jan AI, then query via curl
jan install qwen-small
jan serve --port 1337
curl http://localhost:1337/v1/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen-small", "prompt":"Hello Jan AI!"}'

Hybrid Cloud Configuration

Need bleeding-edge performance or access to the very latest models? Configure your OpenAI or Anthropic API keys and Jan AI will automatically route heavyweight requests to the cloud—while all your privacy-sensitive queries stay local.

Intuitive GUI & CLI Support

A clean, user-friendly interface handles model downloads, context-length sliders, and performance metrics (tokens/sec). For automation or headless setups, every action is mirrored by a powerful CLI.

Open-Source Auditability

Everything’s on GitHub under Apache-2.0—inspect the code, verify there’s no unwanted telemetry, and even contribute your own improvements.

Installation and Getting Started

Download Installer
Grab the Windows .exe, macOS package, or Linux binary from the official Jan AI releases page.
Verify Requirements
- 3B-parameter models: 8–16 GB RAM
- 7B+ models: 16–32 GB RAM + (optional) GPU acceleration
- If building from source, install Node.js and Rust.
Run Installer / Extract Binary
- Windows: double-click the .exe
- macOS/Linux: unpack and run ./jan-ai
Launch Jan AI
Open the GUI, head to Models → Browse, pick a model (e.g., qwen-small), and download.
Configure Settings
- Toggle hybrid-cloud API keys
- Adjust context length & token limits
- Confirm or change server port (default: 1337)
Test Inference
Try a few prompts in the built-in chat. Then disable your network adapter to confirm offline functionality.

Tips for Effective Use

Start Small: Begin with 3B–7B models to gauge CPU/RAM impact.
Monitor Throughput: Watch tokens/sec in the GUI; downscale if it’s too slow.
Offline Validation: Disconnect and test your core workflows before deploying in secure environments.
Hybrid Workflows: Clearly tag which prompts go to cloud models in your logs.
Secure the Server: If you bind to 0.0.0.0, firewall the port or add local auth.
Stay Updated: Check GitHub often for new features and bug fixes.