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Thunder Compute 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 Thunder Compute 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 Thunder Compute does not close: Thunder Compute has the better local-editor story, full stop: a real VS Code, Cursor and Windsurf extension with a connect button, which we do not have. Aquanode's argument is not the editor: it is that a Thunder snapshot stays inside Thunder, while an Aquanode snapshot restores onto a different provider.
To be fair, Thunder Compute's real strength is real: A genuine local IDE integration: an installable VS Code / Cursor / Windsurf extension that connects you to a remote GPU from the editor. Aquanode has no editor extension. Our CLI writes a managed SSH alias and that is all; Remote-SSH working at all is a side effect of the alias existing, not a feature we built.
Live rates for the GPUs training wants
| GPU | From (per GPU/hr) |
|---|---|
| H100 | $2.98 |
| H200 | $3.98 |
| B200 | $9.35 |
| A100 | $1.26 |
Live per-GPU rates from Aquanode's marketplace. Refreshes every 6 hours.
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 Thunder Compute
Training alternatives to other clouds
Related reading: H100 pricing and specs, H100 vs H200, A100 vs H100, H100 and H200: SXM vs NVL vs PCIe, and The best GPUs for AI, ranked.
