Run Powerful AI 100% Offline: Meet Jan, Your Private On‑Device Assistant (Windows, macOS, Linux)

What if you could run ChatGPT‑style AI right on your laptop—no internet, no data sharing, and total control over models and costs? That’s the promise of Jan. It’s a fast, privacy‑first AI assistant that can run 100% offline on your device. You can download large language models (LLMs) like Llama, Gemma, or Qwen and use them locally. And when you do want cloud muscle, Jan connects to providers like OpenAI, Anthropic, Mistral, and Groq—all under one roof.

In this guide, I’ll show you how to get started, what makes Jan different, and how to use it for real‑world work. If you care about privacy, performance, or predictability, this is worth your next 10 minutes.

Let’s dive in.

What Is Jan? A Private AI That Runs On Your Machine

Jan is a desktop AI app that gives you the best of both worlds:

Run local AI models offline with full privacy.
Or plug in cloud APIs for cutting‑edge performance when you need it.
Build custom assistants and even connect other apps via an OpenAI‑compatible local API.

Here’s a snapshot of what Jan includes out of the box:

Local AI Models: Download and run LLMs (Llama, Gemma, Qwen, and more) from Hugging Face.
Cloud Integration: Connect to OpenAI, Anthropic, Mistral, Groq, and others.
Custom Assistants: Create specialized AI agents for your specific tasks and workflows.
OpenAI‑Compatible API: A local server at http://localhost:1337 for use with your existing tools and SDKs.
Model Context Protocol (MCP): Integrate with the Model Context Protocol for enhanced capabilities and tool integrations.
Privacy First: When you choose local models, everything runs offline on your device.

Here’s why that matters: you control your data, avoid surprise bills, and keep your workflows resilient—even without internet.

Why Run AI Locally? Speed, Privacy, and Control

Running AI on your device isn’t just a geeky flex. It’s practical.

Privacy: Your prompts and documents never leave your machine when using local models.
Cost Control: Local runs don’t rack up per‑token fees.
Speed and Reliability: No network latency or rate limits. It just responds.
Ownership and Portability: Use the model you want, when you want. Keep working on a plane, in a lab, or behind strict firewalls.
Hybrid Flexibility: Blend local and cloud models, per project or per assistant.

If you’re a developer, researcher, writer, or security‑minded team, this gives you predictability with zero compromise on capability.

Quick Start: Download Jan for Your OS

The easiest path is a direct download. Jan offers stable and nightly builds for major platforms. Grab it from jan.ai or the official GitHub Releases.

Windows: Download “jan.exe”
macOS: Download “jan.dmg”
Linux (deb): Download “jan.deb”
Linux (AppImage): Download “jan.AppImage”

Nightly builds ship faster updates. Stable builds are, well, more stable. If you value reliability, start with stable. If you like living on the edge, try nightly.

Once you install and open Jan, you can: – Choose a local model to download. – Or connect cloud keys (OpenAI, Anthropic, Mistral, Groq). – Start chatting or create a custom assistant.

Tip: If your OS shows a security prompt for apps from the internet, approve the install. This is common for new desktop apps.

Installation Notes by Platform

A few platform‑specific tips to smooth your setup.

Windows

Download “jan.exe” and run the installer.
If Windows SmartScreen warns you, choose “More info” → “Run anyway.”
For best performance, ensure your GPU drivers are up to date:
NVIDIA drivers
AMD drivers
Intel Arc drivers

macOS

Download “jan.dmg,” open it, and drag the app into Applications.
If macOS blocks the app, go to System Settings → Privacy & Security → “Open Anyway.”
Apple Silicon (M1/M2/M3) provides great on‑device performance; local models shine here thanks to Metal acceleration (Metal).

Linux

Debian/Ubuntu: Download “jan.deb” and install it (e.g., via your package manager or “sudo dpkg -i jan.deb”).
AppImage: Make the file executable and run it (“chmod +x jan.AppImage”).
GPU acceleration varies by distro and drivers. Keep your system updated.

If you run into issues, check Jan’s troubleshooting docs or the #🆘|jan-help channel in the community Discord (see the link in the GitHub repo).

System Requirements: What You Need for a Smooth Experience

Here’s the baseline guidance for local LLMs:

macOS: 13.6+
3B models: 8 GB RAM
7B models: 16 GB RAM
13B models: 32 GB RAM
Windows: 10+
GPU support for NVIDIA/AMD/Intel Arc recommended
Linux: Most distributions
GPU acceleration available with compatible drivers

Bigger models generally deliver better quality but demand more memory. Start smaller (e.g., 3B or 7B) and scale up as your hardware allows.

Picking a Model: Llama, Gemma, Qwen, and Friends

The right model depends on your tasks and machine. You can browse and download models from Hugging Face.

General guidance: – Llama family: Strong general reasoning and writing quality. – Gemma: Lightweight models by Google, good quality for size. – Qwen: Great multilingual support and balanced performance.

Consider: – Size: 3B/7B/13B indicates parameter count. Bigger is usually better but heavier. – Quantization: Smaller quantized variants (like 4‑bit) run faster and fit in less memory. – Use case: Coding, Q&A, summarization, or creative writing.

Pro tip: Keep a “fast” small model for quick drafts and a “strong” larger model for final passes.

Local vs Cloud: When to Use Which

Jan lets you mix local and cloud seamlessly.

Use local when: – You need privacy and offline reliability. – Your prompts include sensitive data. – You’re doing lots of iterations and want predictable costs.

Use cloud when: – You need frontier‑level performance for complex tasks. – You want the latest model capabilities (like advanced reasoning or long context windows). – Latency is fine and you have API credits.

You can set model/provider preferences per assistant or per task. It’s your call.

Build Custom Assistants for Your Own Workflows

Inside Jan, you can create specialized assistants. Think of them as reusable “profiles” tuned for specific tasks:

A legal assistant that always cites sources and asks clarifying questions.
A code buddy that focuses on TypeScript and Jest tests.
A research aide that summarizes papers and extracts takeaways.

Customize: – System prompts and behavior. – Default models (local or cloud). – Tools/integrations via MCP.

Here’s why that matters: you reduce repetitive setup, get more consistent outputs, and keep your work organized.

Use Jan as a Local OpenAI‑Compatible API (Port 1337)

Already have scripts that call the OpenAI API? Point them to Jan’s local server and keep working—offline.

Base URL: http://localhost:1337/v1
Works with OpenAI‑style SDKs and routes.

Example: curl a chat completion

curl http://localhost:1337/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "your-local-model",
    "messages": [{"role": "user", "content": "Write a 2-line haiku about offline AI."}]
  }'

Python (OpenAI‑style SDK):

from openai import OpenAI

client = OpenAI(
    base_url="http://localhost:1337/v1",
    api_key="sk-local"  # placeholder, Jan doesn't require a real key for local
)

resp = client.chat.completions.create(
    model="your-local-model",
    messages=[{"role": "user", "content": "Summarize Jan in one sentence."}]
)
print(resp.choices[0].message.content)

Node/TypeScript:

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "http://localhost:1337/v1",
  apiKey: "sk-local"
});

const res = await client.chat.completions.create({
  model: "your-local-model",
  messages: [{ role: "user", content: "List 3 benefits of running AI offline." }]
});
console.log(res.choices[0].message.content);

This is a game‑changer if you’re integrating AI into tools, backends, or notebooks and want portable, private infrastructure.

MCP Integration: Extend Jan with the Model Context Protocol

Jan supports the Model Context Protocol (MCP), an open standard for connecting AI models to tools, data sources, and apps in a structured way.

In plain English: MCP lets your assistant securely access external capabilities—like file systems, databases, or custom tools—without brittle, one‑off glue code. If you’re building richer assistants, MCP is a powerful way to scale their reach.

Don’t Want Binaries? Build Jan from Source

If you like the scenic route—or want to contribute—building Jan from source is straightforward.

Prerequisites: – Node.js ≥ 20.0.0 – Yarn ≥ 1.22.0 – GNU Make ≥ 3.81 – Rust (for Tauri)

Clone the repo:

git clone https://github.com/menloresearch/jan
cd jan

Option 1: Run with Make

This handles installs, builds, and launch in one command:

make dev

Other targets: – make build — Production build – make test — Run tests and linting – make clean — Delete everything and start fresh

Option 2: Run with Mise (Easiest)

Mise ensures correct versions of Node.js, Rust, and other tools are installed automatically.

git clone https://github.com/menloresearch/jan
cd jan

# Install mise if needed
curl https://mise.run | sh

# Install tools and start development
mise install
mise dev

Available commands: – mise dev — Full development setup and launch – mise build — Production build – mise test — Run tests and linting – mise clean — Delete everything and start fresh – mise tasks — List all available tasks

Option 3: Manual Commands

Prefer full manual control? No problem:

yarn install
yarn build:tauri:plugin:api
yarn build:core
yarn build:extensions
yarn dev

This sequence installs dependencies, builds core components, and launches the app.

Performance Tips: Get the Most from Local Models

A few practical tips to keep things snappy:

Match model size to your hardware. Start small, then scale.
Use quantized models (e.g., 4‑bit) for memory‑constrained machines.
Keep GPU drivers up to date:
NVIDIA CUDA
AMD ROCm
Intel Arc
Close other heavy apps when running large models.
For macOS, Apple Silicon plus Metal gives great acceleration out of the box.

Think of it like video editing: the right codec (quantization), hardware (GPU/RAM), and scene complexity (prompt length) determine smooth playback.

Troubleshooting: When Things Go Sideways

It happens. Here’s a quick checklist:

Check the troubleshooting docs and logs. Copy error logs and system specs before asking for help.
Verify versions: Node 20+, Yarn 1.22+, Make 3.81+, Rust installed.
If building from source, run make clean or mise clean, then rebuild.
Update GPU drivers and try a smaller model if you hit memory errors.
On macOS, allow the app in Privacy & Security if it’s blocked.
Test the local API: open http://localhost:1337 in a browser; if it responds, the server is running.
Still stuck? Ask in the Discord #🆘|jan-help channel (link in the GitHub repo).

Empathetic note: debugging AI stacks can feel like wrestling shadows. Logs and exact error messages are your best allies.

Practical Use Cases You Can Start Today

Private research assistant for PDFs and notes. Keep sensitive material on‑device.
Coding buddy for local repos without uploading source.
Brainstorming and drafting without per‑token fees.
Offline knowledge worker on flights, in labs, or on secure networks.
Hybrid pipelines: quick local drafts, cloud polish on demand.

Once you’ve set up a few custom assistants, you’ll stop fiddling and start shipping.

Security and Privacy: You’re in Control

Jan is privacy‑first by design: – When you select local models, everything runs offline on your machine. – You can decide when to use cloud providers and which keys to enable. – The local API runs on your device, not a public endpoint.

This control is key for regulated industries, R&D, or anyone who values digital sovereignty.

How Jan Compares to Other Options

There are other ways to run local LLMs—CLI tools, web UIs, or code‑heavy frameworks. Jan stands out by: – Offering a polished desktop experience that non‑developers can use. – Providing hybrid mode (local + multi‑cloud) in one place. – Being OpenAI‑API compatible for easy integrations. – Supporting MCP to extend assistants with tools and data.

If you’ve outgrown copy‑paste prompts and want a maintainable, private setup, Jan hits a sweet spot.

Frequently Asked Questions

Q: Is Jan really 100% offline?
A: Yes—when you choose local models, Jan runs entirely on your device. No data leaves your machine. If you connect cloud providers, those specific requests use the internet, but you control when and how.

Q: Where do I download Jan?
A: From jan.ai or the official GitHub Releases. Choose your OS and download the installer.

Q: Which local models work best on lower‑end hardware?
A: Try 3B or 7B quantized models (4‑bit) from Hugging Face. They’re fast and memory‑friendly.

Q: Can I use Jan with the OpenAI SDK?
A: Yes. Point your SDK at http://localhost:1337/v1 and use an arbitrary API key (e.g., sk-local). Jan speaks the OpenAI API.

Q: What’s the difference between stable and nightly builds?
A: Stable builds prioritize reliability. Nightly builds include the newest features and fixes but may be less tested. If you’re new, start with stable.

Q: Do I need a GPU?
A: Not strictly. CPUs can run smaller models, though GPUs help with speed and larger models. On macOS, Apple Silicon offers strong on‑device performance.

Q: How do I report issues or get help?
A: Check the troubleshooting docs, gather logs and system specs, and ask in the Discord #🆘|jan-help channel. The Discord link is available via the GitHub repo.

Q: Does Jan support tools or plug‑ins?
A: Yes, via the Model Context Protocol (MCP). This lets assistants securely access tools and data sources.

Q: Can I build Jan from source?
A: Absolutely. You’ll need Node 20+, Yarn 1.22+, Make 3.81+, and Rust (for Tauri). Use make dev or mise dev for a one‑command setup.

Q: Is Jan open source?
A: You can view and contribute to the project on the official GitHub repository.

Final Takeaway

Jan gives you private, powerful AI on your own hardware—no compromises. Run models locally for privacy and speed, or switch to cloud providers when you need extra horsepower. Build custom assistants, integrate with your tools via the OpenAI‑compatible API, and stay in full control of your data and costs.

Ready to try it? Download Jan from jan.ai or the official GitHub Releases. If you found this helpful, keep exploring our guides—or subscribe for more deep dives on local AI and practical workflows.

Discover more at InnoVirtuoso.com

I would love some feedback on my writing so if you have any, please don’t hesitate to leave a comment around here or in any platforms that is convenient for you.

For more on tech and other topics, explore InnoVirtuoso.com anytime. Subscribe to my newsletter and join our growing community—we’ll create something magical together. I promise, it’ll never be boring!

Stay updated with the latest news—subscribe to our newsletter today!

Thank you all—wishing you an amazing day ahead!