Vast.ai Alternatives for Training

Training a model from scratch or continuing a pre-training run, usually for days rather than hours.

What training actually needs

80 GB+
VRAM floor
Often
Multi-GPU
If it checkpoints
Survives losing the box
Matters a lot
Environment persistence

Why people look past Vast.ai 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 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. Aquanode supports Vast.ai among other providers, so the honest framing is not us-versus-them on price: it is that an environment built on a Vast machine can be snapshotted and moved off it.

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 training wants

GPU
From (per GPU/hr)
Providers
H100
$1.99
8
H200
$3.59
6
B200
$6.79
3
A100
$0.668
7

Live per-GPU rates from Aquanode's marketplace. Refreshes hourly.

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

Training alternatives to other clouds

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