Google Cloud GPU VMs Alternatives for Notebooks
Interactive exploration in Jupyter, where the session is the work and an idle timeout is the enemy.
What notebook work actually needs
Why people look past Google Cloud GPU VMs for this
A notebook box accumulates state nobody wrote down: installed packages, mounted data, half-finished cells. Losing it to a disconnect is the whole complaint. That is exactly the gap Google Cloud GPU VMs does not close: Google Cloud's GPU VMs are the right choice when the rest of your stack is on GCP: BigQuery, GCS and Vertex adjacency are the product. As a standalone GPU rental it is priced as enterprise cloud, and the environment is a GCP disk image that stays in GCP.
To be fair, Google Cloud GPU VMs's real strength is real: Adjacency to BigQuery, GCS, Vertex AI and the rest of the platform, plus enterprise compliance and support.
Live rates for the GPUs notebook work wants
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today as a Pod: the console's own notebook workload card preselects the right template for this job.
Restoring an environment requires a snapshot that already exists. Stopping a deployment yourself captures it on the way out, so you can bring it back later on any provider. A provider-side termination is different: it is only recoverable if you had already switched automated snapshots on for that deployment, and it costs you the work since the last one. Automated snapshots are opt-in, nothing runs until you start it, and with none running there is nothing to restore.
More alternatives pages
Other workloads on Google Cloud GPU VMs
Notebooks alternatives to other clouds
