Deploy on JarvisLabs
Run Laboratory OS on JarvisLabs, a GPU cloud with fast 90-second deployments. JarvisLabs supports running custom Docker containers directly.
1. Choose a GPU
Log in to the JarvisLabs dashboard and select a GPU.
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.
2. Configure the Instance
In the deployment form, set up the container:
Docker Image:
openlaboratoryorg/laboratory-osEnvironment Variables:
| Key | Value |
|---|---|
UPLINK_API_KEY | Your account key from openlaboratory.com. The lab connects through it and serves on your *.laboratory.computer URL. |
Storage: Allocate enough disk for your models. JarvisLabs provides persistent storage that carries over between sessions.
No inbound ports or firewall rules are needed — Laboratory OS uses an outbound tunnel. You can block all inbound traffic entirely.
3. Launch and Connect
Click Launch. The instance starts and the container pulls automatically — this usually takes under a minute.
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.computerFrom 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. Stopping
Stop the instance from the JarvisLabs dashboard when not in use. Your persistent disk and installed packages are preserved across restarts.
Tip: JarvisLabs provides a JupyterLab interface by default. Laboratory OS runs alongside it — look for the Laboratory OS URL in the container logs.