Google Cloud GPU VMs Alternatives for Training
Training a model from scratch or continuing a pre-training run, usually for days rather than hours.
What training actually needs
Why people look past Google Cloud GPU VMs for this
The checkpoint is the cheap part to move; the built environment, the dataset cache and the exact CUDA and framework versions around it are what take a day to rebuild. 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 training 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 box 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
Training alternatives to other clouds
