How to use Menlo Jan-Nano for free?
In today’s GenAI led era, simply retrieving facts isn’t enough. What we need is deep research — the ability to go beyond surface-level answers, analyze complex sources, synthesize insights, and do it all with context-awareness and precision. This is where the next frontier of AI lies.
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OpenAI recently introduced DeepResearch, a powerful system designed to supercharge researchers with AI tools that can read documents, browse the web, and cite sources — all in one seamless workflow. It’s a major step toward turning AI into a true thinking partner, not just a chatbot.
But DeepResearch comes with a catch: it’s tightly integrated into the OpenAI ecosystem, relies on cloud infrastructure, and often comes with usage limits or privacy concerns.
Enter Jan-Nano: Local Deep Research LLM, No Cloud Required
Jan-Nano is a small but mighty AI model with 4 billion parameters, built specially for one thing: deep research. Whether you’re digging into complex topics or just want smart, reliable answers, Jan-Nano is designed to help — no cloud required.
Instead of depending on massive server farms, Jan-Nano integrates with Model Context Protocol (MCP) servers, enabling real-time tool use (like live search) — just like DeepResearch — but with no data ever leaving your computer.
OpenAI DeepResearch offers cutting-edge research workflows — but only within the OpenAI cloud. Also, its paid !
Jan-Nano brings similar power to your own device, putting you in control of your research, tools, and data.
Jan-Nano vs other LLMs
Jan-Nano is a game changing LLM and can be your tracer-bullet for local deep-research task or even MCP compatible
- Runs locally (Jan-Nano) vs Cloud-based only (OpenAI DeepResearch)
- Full privacy, no data leaves device (Jan-Nano) vs Data processed on OpenAI servers (OpenAI DeepResearch)
- MCP-based local tool integration (Jan-Nano) vs Built-in cloud tools (OpenAI DeepResearch)
- Requires moderate local GPU (e.g., 8–16GB VRAM) (Jan-Nano) vs No hardware needed, but constant internet required (OpenAI DeepResearch)
- Free and open-source (Jan-Nano) vs Subscription-based or usage-limited (OpenAI DeepResearch)
- Fully customizable and tweakable (Jan-Nano) vs Closed, fixed interface (OpenAI DeepResearch)
- 4B parameter lightweight model (Jan-Nano) vs Larger GPT-class models (OpenAI DeepResearch)
- Best for offline, privacy-conscious, technical users (Jan-Nano) vs Best for general users seeking AI-enhanced research workflows (OpenAI DeepResearch)
Benchmarks
Jan-Nano was tested using a benchmark called SimpleQA — a way to measure how well models answer straightforward research questions. But unlike most tests, this one included live tools (via the MCP server), showing how Jan-Nano performs in real-world research situations.
Result? It performs impressively well, especially considering it’s running locally without cloud power
System Requirements
Want to run Jan-Nano? Here’s what you’ll need:
Minimum Requirements
- 8GB RAM (with ultra-lightweight model setting: iQ4_XS)
- 12GB GPU VRAM (for standard Q8 setting)
- A CUDA-compatible GPU (NVIDIA recommended)
Recommended Setup
- 16GB+ RAM
- 16GB+ GPU VRAM
- RTX 30 or 40 series GPU
- Latest CUDA drivers
Even if you only have 8GB RAM and a modest setup, Jan-Nano can still run — just use the lighter quantized models.
How to use Jan-Nano?
The model is open-sourced and can be accessed via given instructions on HuggingFace
Conclusion
Jan-Nano brings the power of deep research AI to your own device — with full privacy, flexibility, and no cloud required. While tools like OpenAI DeepResearch offer powerful features online, Jan-Nano gives you control, speed, and smart research capabilities locally.
If you value privacy and want a lightweight yet capable AI assistant, Jan-Nano is a tool worth trying.
Jan-Nano: The 1st Deep Research LLM was originally published in Data Science in Your Pocket on Medium, where people are continuing the conversation by highlighting and responding to this story.
