Deploy on Nebius

Run Laboratory OS on a Nebius GPU virtual machine. Nebius is an AI-optimized cloud built on NVIDIA infrastructure.

No inbound ports or firewall rules are needed — Laboratory OS uses an outbound tunnel. You can block all inbound traffic entirely.

1. Create a GPU Instance

Platform: Select a GPU platform.

Choosing a GPU: More VRAM lets you run larger models — 16 GB handles most image generation, 24-48 GB covers mid-size LLMs, and 100 GB+ is needed for large LLMs.

Image: Choose an Ubuntu 22.04 image.

Allocate at least 100 GB of storage. 200+ GB recommended if you plan to download multiple models or LLMs.

Network: No inbound rules are needed.

2. Install Dependencies

SSH into the VM:

ssh ubuntu@<vm-public-ip>

Docker

curl -fsSL https://get.docker.com | sh
sudo usermod -aG docker $USER
newgrp docker

Verify:

docker run --rm hello-world

If you see “Hello from Docker!”, the installation was successful.

NVIDIA Drivers

Note: Nebius GPU-optimized images may ship with NVIDIA drivers pre-installed. Run nvidia-smi first — if it works, skip this section.

sudo apt-get update
sudo apt-get install -y ubuntu-drivers-common
sudo ubuntu-drivers autoinstall
sudo reboot

After reboot, confirm the driver is loaded:

nvidia-smi

You should see your GPU listed with its driver version and VRAM.

Troubleshooting: If nvidia-smi is not found after install, make sure you have rebooted — the reboot after ubuntu-drivers autoinstall is required. If you see a Driver/library version mismatch error, reboot to resync the driver and CUDA library versions.

NVIDIA Container Toolkit

# Add the NVIDIA package repository
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | \
  sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
  sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
  sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# Install
sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

# Configure Docker runtime and restart
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

Verify:

docker run --rm --gpus all nvidia/cuda:12.0-base-ubuntu22.04 nvidia-smi

If you see nvidia-smi output inside the container, everything is set up correctly.

Troubleshooting: If you see could not select device driver “nvidia”, run sudo nvidia-ctk runtime configure --runtime=docker followed by sudo systemctl restart docker. If the GPU is visible on the host but not inside the container, make sure you are passing --gpus all to docker run.

3. Run Laboratory OS

docker run --gpus all \
  -e UPLINK_API_KEY='your-account-key' \
  openlaboratoryorg/laboratory-os

Check the container logs for your assigned slug — it appears within the first 30 seconds. Then sign in to your account at:

https://app.laboratory.computer

From there you'll see your running instance and can open the Laboratory OS desktop. Access is gated by your SSO login rather than a shared token, so only authenticated account holders can connect.

4. Cost Management

Stop the VM from the Nebius console when not in use. The boot disk is retained and billing pauses for compute. Restart the VM and the container resumes if you used --restart unless-stopped.

Tip: Use Preemptible VMs for up to 70% savings on interruptible workloads.