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Vast.ai Alternatives for Model Evaluation
Benchmarking a model against a declared task list, where the run must be reproducible to be worth anything.
What model evaluation actually needs
Why people look past Vast.ai for this
Numbers are only comparable if the environment is, so an eval that moves boxes has to carry its exact stack with it or the results are not comparable to the last run. 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 model evaluation wants
| GPU | From (per GPU/hr) |
|---|---|
| H100 | $2.19 |
| A100 | $1.16 |
| L40S | $0.869 |
| RTX 4090 | $0.374 |
Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.
How you run it here today
Run it today with the seeded lm-eval-harness 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
Model Evaluation 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.
