Deploy on Akamai (Linode)

Run Laboratory OS on an Akamai Cloud Computing GPU instance (formerly Linode). Simple pricing and straightforward management.

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

Image: Choose Ubuntu 22.04 LTS.

Region: Select a region with GPU availability (not all regions offer GPU instances).

Plan: Under Shared CPU or Dedicated CPU, look for GPU-enabled plans.

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.

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

Firewall: No inbound rules are needed. Akamai’s cloud firewall can be left with all inbound traffic blocked.

2. Install Dependencies

SSH into the instance:

ssh root@<instance-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

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

Power off or delete the instance from Cloud Manager when not in use. Linode bills by the hour — a running instance charges whether or not you’re actively using it.

Tip: Linode offers flat, predictable pricing with no hidden egress fees.