Jan-Nano is a lightweight yet powerful 4-billion parameter language model tailored for deep, research-heavy workloads. Built by Menlo Research, this model isn’t just about generating text — it’s designed to think clearly, answer precisely, and connect well with research tools thanks to its MCP (Model Context Protocol) compatibility. It’s fine-tuned from Qwen3-4B, which means it benefits from a solid foundation, but Jan-Nano takes it a step further with performance tweaks specifically for structured queries and fact-first tasks.
Whether you’re building a private assistant, running complex Q&A pipelines, or integrating with tool-augmented workflows — Jan-Nano delivers consistent, grounded responses without the bloat of mega-scale models.
Resources
Link: https://huggingface.co/Menlo/Jan-nano
Jan-Nano GPU Configuration Table
| GPU Model | vCPUs | RAM (GB) | VRAM (GB) | Precision | Recommended Use Case |
|---|
| RTX A6000 | 64 | 48 | 48 | BF16 / FP16 | Ideal for full-precision inference (no quant needed) |
| A100 40GB | 96 | 256 | 40 | BF16 / FP16 | Efficient inference and multi-user workloads |
| T4 | 16 | 16 | 16 | 4-bit only | Use quantized model (ggml/GGUF) only |
| RTX 3090 / 4090 | 24 | 32–64 | 24 | BF16 / FP16 | Good for local deployment and testing |
| H100 (80GB) | 192 | 512 | 80 | BF16 / FP16 | Overkill, but great for high-throughput workflows |
Step-by-Step Process to Install Jan-Nano Locally
For the purpose of this tutorial, we will use a GPU-powered Virtual Machine offered by NodeShift; however, you can replicate the same steps with any other cloud provider of your choice. NodeShift provides the most affordable Virtual Machines at a scale that meets GDPR, SOC2, and ISO27001 requirements.
Step 1: Sign Up and Set Up a NodeShift Cloud Account
Visit the NodeShift Platform and create an account. Once you’ve signed up, log into your account.
Follow the account setup process and provide the necessary details and information.
Step 2: Create a GPU Node (Virtual Machine)
GPU Nodes are NodeShift’s GPU Virtual Machines, on-demand resources equipped with diverse GPUs ranging from H100s to A100s. These GPU-powered VMs provide enhanced environmental control, allowing configuration adjustments for GPUs, CPUs, RAM, and Storage based on specific requirements.
Navigate to the menu on the left side. Select the GPU Nodes option, create a GPU Node in the Dashboard, click the Create GPU Node button, and create your first Virtual Machine deploy
Step 3: Select a Model, Region, and Storage
In the “GPU Nodes” tab, select a GPU Model and Storage according to your needs and the geographical region where you want to launch your model.
We will use 1 x RTX A6000 GPU for this tutorial to achieve the fastest performance. However, you can choose a more affordable GPU with less VRAM if that better suits your requirements.
Step 4: Select Authentication Method
There are two authentication methods available: Password and SSH Key. SSH keys are a more secure option. To create them, please refer to our official documentation.
Step 5: Choose an Image
Next, you will need to choose an image for your Virtual Machine. We will deploy Jan-Nano on an NVIDIA Cuda Virtual Machine. This proprietary, closed-source parallel computing platform will allow you to install Jan-Nano on your GPU Node.
After choosing the image, click the ‘Create’ button, and your Virtual Machine will be deployed.
Step 6: Virtual Machine Successfully Deployed
You will get visual confirmation that your node is up and running.
Step 7: Connect to GPUs using SSH
NodeShift GPUs can be connected to and controlled through a terminal using the SSH key provided during GPU creation.
Once your GPU Node deployment is successfully created and has reached the ‘RUNNING’ status, you can navigate to the page of your GPU Deployment Instance. Then, click the ‘Connect’ button in the top right corner.
Now open your terminal and paste the proxy SSH IP or direct SSH IP.
Next, if you want to check the GPU details, run the command below:
nvidia-smi
Step 8: Check the Available Python version and Install the new version
Run the following commands to check the available Python version.
If you check the version of the python, system has Python 3.8.1 available by default. To install a higher version of Python, you’ll need to use the deadsnakes PPA.
Run the following commands to add the deadsnakes PPA:
sudo apt update
sudo apt install -y software-properties-common
sudo add-apt-repository -y ppa:deadsnakes/ppa
sudo apt update
Step 9: Install Python 3.11
Now, run the following command to install Python 3.11 or another desired version:
sudo apt install -y python3.11 python3.11-venv python3.11-dev
Step 10: Update the Default Python3 Version
Now, run the following command to link the new Python version as the default python3:
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.11 2
sudo update-alternatives --config python3
Then, run the following command to verify that the new Python version is active:
python3 --version
Step 11: Install and Update Pip
Run the following command to install and update the pip:
curl -O https://bootstrap.pypa.io/get-pip.py
python3.11 get-pip.py
Then, run the following command to check the version of pip:
pip --version
Step 12: Clone the WebUI Repo
Run the following command to clone the webui repo:
git clone https://github.com/oobabooga/text-generation-webui
cd text-generation-webui
Step 13: Run the One-Click Installer Script
Execute the following command to run the one-click installer script:
bash start_linux.sh
- It will automatically detect your GPU, install everything needed (Python, pip, CUDA toolkits, etc.)
- You’ll be prompted to select your GPU backend (choose
CUDA or NVIDIA GPU).
- Wait for it to finish setting up Python environment + dependencies.
Since our VM uses NVIDIA CUDA GPUs (e.g., A100, H100, A6000), choose:
A
Just type A and hit Enter.
What Happens Next
Once you select option A, the script will:
- Install
torch, vllm, and GPU-specific dependencies
- Prepare the web UI environment
- Prompt you to select or download a model (or you can do that manually)
- Launch the server on
http://127.0.0.1:7860
Step 14: SSH Port Forward
On your machine run the following command for SSH port forward:
ssh -L 7860:127.0.0.1:7860 root@<your_vm_ip>
Then open: http://localhost:7860 in your browser.
Step 15: Download the Model
Run the following command to download the model:
python3 download-model.py Menlo/Jan-nano
Step 16: Go back to the Web UI in browser
- Go to the “Model” tab
- Find
Menlo/Jan-nano
- Click Load
Step 17: Test with Prompts
Conclusion
Jan-Nano proves that you don’t need a monster model to get serious research done. With just 4B parameters, it brings structure, clarity, and tool-ready performance — making it a perfect fit for researchers, devs, and engineers who value precision over fluff. Whether you’re running it locally or deploying on a GPU-powered NodeShift VM, the setup is fast, the performance is stable, and the results speak for themselves.
Now that Jan-Nano is up and running, you’re all set to dive into smarter text generation, context-aware analysis, or even integrate it into your research workflows.
