GPU Training Cost and Time Calculator
GPU training cost with scaling efficiency and failed runs counted. Doubling GPUs does not halve training time, so the cost per useful hour rises with cluster size.
Also called: training compute cost, gpu hour calculator.
$475,200.00 for 3 runs of 79.2 hours on 8 GPUs. A single run costs $158,400.00. Scaling at 85% means 285.1 GPU hours are lost to coordination, worth $71,280.00. Scaling efficiency below ninety percent means a meaningful share of the spend buys coordination rather than computation.
How this is calculated
The bill is GPU count times hours times rate, multiplied by the number of runs you actually need. Two realities inflate it. Multi-GPU scaling is never perfect: communication overhead means eight GPUs deliver perhaps six and a half GPUs of useful work, and the gap is paid for. And the first run rarely works, so budgeting for one run understates by the failure rate. Spot pricing cuts the rate substantially and adds restart risk, which is why checkpointing overhead is a separate input.
imperfect scaling means the wall-clock saving from more GPUs is less than proportional, and the cost is not- n
- GPU count
- eta
- Scaling efficiency
Method and limits
What it assumes
- A constant scaling efficiency, which typically falls as the cluster grows.
What it deliberately does not model
- Scaling efficiency depends heavily on model architecture, batch size and interconnect.
- Spot preemption can lose work between checkpoints entirely.
- Data preparation and evaluation compute are not included.
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Frequently asked questions
- Does doubling GPUs halve the time?
- No. Communication overhead means the speedup is sublinear, so the cost per useful GPU hour rises with cluster size even though wall-clock time falls.
- Should I budget for one run?
- No. The first attempt rarely produces a usable model, and budgeting for a single run is the most common way training costs overrun.