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Jupyter on a GPU VPS is a CUDA-accelerated deployment. Jupyter is a general GPU-accelerated workload. Make sure your software has CUDA support and that your driver / runtime versions match the workload requirements for Jupyter.

Deploy a GPU VPS with the NVIDIA Tesla P40, SSH in, and run pip install jupyterlab torch && jupyter lab --ip=0.0.0.0 --allow-root. Your Jupyter environment is ready in minutes with full GPU acceleration.

Our GPU VPS ships with 24 GB GDDR5X VRAM on the NVIDIA Tesla P40, which is sufficient for most Jupyter workloads. Multi-GPU configurations are available on request.

GPU VPS plans are billed monthly with no lock-in contracts and can be cancelled anytime. Contact us for current GPU pricing tiers.

Yes — you have full root on the GPU VPS. Run whatever fits inside the 24 GB VRAM and the available RAM / storage budget alongside Jupyter.

Yes. Full root SSH on every GPU VPS — install drivers, swap CUDA versions, customize the environment for Jupyter however you need.

GPU VPSs ship with a recent CUDA runtime and the matching NVIDIA driver pre-installed. You can pin or upgrade CUDA versions as required by your Jupyter workload.

Yes — your Jupyter GPU VPS is a long-running persistent server, not an ephemeral instance. Models, configs, and data stay on the SSD between sessions.

Keep working data on the VPS SSD for fast access during Jupyter runs; back up finished artifacts (weights, generations, embeddings) off-server via snapshots or object storage for safety.

Yes — plan upgrades are instant from your control panel; the GPU itself can be swapped to a larger tier on request. Your Jupyter install carries over.

Yes. Automated daily backups are an add-on; manual snapshots are free. Useful for long Jupyter training runs where you want a checkpointable server state.

Yes — 30-day money-back guarantee on every plan including GPU. Try Jupyter on a GPU VPS risk-free.