Instance Infrastructure
Hardware Specifications
- GPU and CPU configs: Check pricing page for latest availability
- Location: North America
Pre-installed Software
- CUDA: Version 13.0
- CUDA Driver: Version 580
- PyTorch: Version 2.9.0+cu128
- JupyterLab: Pre-installed
- Additional scientific Python libraries (NumPy, Pandas, etc.)
Do not attempt to reinstall CUDA. If compatibility issues arise, use a venv and change the versions of your other dependencies (e.g., PyTorch) rather than modifying the CUDA libraries.
Storage
- Persistent Disk: Your home directory and OS. Preserved across modifications and included in snapshots. Can be expanded but not shrunk.
- Ephemeral Storage: Optional fast local NVMe disk mounted at
/ephemeral. Not included in snapshots and lost when the instance is modified or deleted. Ideal for model weights, caches, and scratch files. See Ephemeral Storage.
| Storage Type | Prototyping Range | Production Range |
|---|
| Persistent Disk | 100 - 400 GB | 100 - 1000 GB |
| Ephemeral Storage | 0 - 300 GB | 0 - 500 GB |
Networking
- Egress/Ingress: 7 Gbps
- IP Address: Dynamic
Port Access
- Public URLs (CLI): Use
tnr ports forward to expose HTTP services at https://<uuid>-<port>.thundercompute.net with automatic HTTPS and DDoS protection. See Port Forwarding for details.
- Local tunneling (CLI): Use
tnr connect <instance_id> -t <port> to tunnel ports to your local machine
- VS Code: Use the built-in port forwarding feature
