Run Jupyter notebooks on an NVIDIA Tesla P40 (24 GB VRAM, Pascal architecture) for interactive data science and ML work. GPU servers are not orderable yet; contact us to be notified when they are.
$ pip install jupyterlab torch && jupyter lab --ip=0.0.0.0 --allow-root
# Example install command
Jupyter notebooks on a GPU server give you an interactive environment where notebook cells can use the GPU. Useful for experiments and model prototyping.
Use the GPU from notebook cells once CUDA and your framework are installed.
JupyterLab with terminal, file browser and extensions.
You install PyTorch, TensorFlow, scikit-learn, pandas and whatever else you need.
Reach your notebooks from a browser; secure them with a token, password or SSH tunnel.
Jupyter can use an NVIDIA GPU to speed up its work. VPS.org GPU servers are not available to order yet; contact us to hear when they are. 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.
GPU servers are not available to order yet. Today you can run Jupyter on a regular CPU plan, where it works far more slowly than on a GPU: SSH in and run pip install jupyterlab torch && jupyter lab --ip=0.0.0.0 --allow-root.
VRAM needs for Jupyter depend on the model and batch size; check the project documentation for current figures.
GPU pricing will be published when GPU servers can be ordered. Contact us if you want to hear when that happens.
Yes. Every VPS.org server comes with full root access, so you can run other tools alongside Jupyter within the server's RAM and storage.
Every VPS.org server comes with full root SSH access, so you can install drivers, libraries and Jupyter however you need.
No. No server comes with a CUDA runtime or NVIDIA driver preinstalled, and GPU servers are not available to order yet. You install the versions Jupyter needs yourself.
Yes. VPS.org servers are persistent virtual machines, not ephemeral instances: Jupyter models, configs and data stay on the server's SSD storage until you delete them.
Keep working data on the server's SSD for fast access during Jupyter runs, and copy finished artifacts (weights, generations, embeddings) to storage outside VPS.org; snapshots and backups stay on the same storage cluster.
You can upgrade any VPS.org server to a larger plan from the control panel; the resize restarts it and your Jupyter install carries over. GPU servers are not available yet.
Yes. Backups are a paid add-on (daily +30% of the plan price, kept 7 days; weekly +20%, kept 28 days) and snapshots are free, which is useful before long Jupyter runs. Both stay on the same storage cluster as the server.
Contact support within 30 days of your first purchase to request a refund. GPU servers are not sold yet, so no GPU order is involved.
GPU servers are not orderable yet. Tell us about your workload and we will let you know when they are.