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Vast.ai 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 Vast.ai 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 Vast.ai does not close: Vast.ai is usually the cheapest raw dollar-per-hour on the market, because hosts bid against each other and you absorb the variance in reliability. The honest framing is not us-versus-them on price: it is whether the environment you build survives the box it was built on.
To be fair, Vast.ai's real strength is real: Raw price. A host-bid marketplace with interruptible instances will beat brokered on-demand capacity on the low end, and that is by design.
Live rates for the GPUs fine-tuning wants
| GPU | From (per GPU/hr) | Providers |
|---|---|---|
| H100 | $2.19 | 8 |
| A100 | $1.21 | 5 |
| L40S | $0.712 | 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 Vast.ai
Fine-Tuning alternatives to other clouds
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
