Thunder Compute 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 Thunder Compute 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 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 fine-tuning wants
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 Thunder Compute
Fine-Tuning alternatives to other clouds
