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Google Cloud GPU VMs Alternatives for Fine-Tuning
Adapting an existing base model to your own data, typically LoRA or a full fine-tune over hours.
What fine-tuning actually needs
Why people look past Google Cloud GPU VMs for this
Base weights are re-downloadable but slow; the adapter checkpoints and the dataset you spent time cleaning are not something you want to re-derive. 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 fine-tuning wants
| GPU | From (per GPU/hr) | Providers |
|---|---|---|
| H100 | $2.19 | 7 |
| A100 | $1.17 | 4 |
| L40S | $0.869 | 5 |
| RTX 4090 | $0.374 | 3 |
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded lora-finetune job recipe: a real, publicly-imaged container Aquanode can queue directly, not a template we're promising to build later.
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
Fine-Tuning alternatives to other clouds
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
