A/B Testing GPU Performance (Monthly) Calculator FAQ
Answers to common questions about monthly GPU throughput comparisons, utilization, GPU cost estimates, and cost per million tasks.
Use these questions and answers to understand the inputs, calculations, assumptions, and interpretation of a monthly GPU configuration A/B test. Results are operational estimates and should be validated with representative benchmarks.
Using the Calculator
Questions about the purpose of the monthly GPU comparison and the information it needs.
What does the A/B Testing GPU Performance (Monthly) Calculator compare?
It compares two GPU configurations by estimated monthly workload, GPU cost, cost per million tasks, and B's throughput difference relative to A.
What input should I use for throughput per GPU?
Use a measured average number of completed tasks per hour for one GPU under the workload and settings you want to compare.
Can configuration A and B have different numbers of GPUs?
Yes. The calculation uses each configuration's own GPU count, per-GPU throughput, and hourly GPU rate.
What can count as a task?
A task can be an inference request, image, record, token batch, training step, or another repeatable work unit, as long as it is consistent between A and B.
Calculation Method
Questions about productive hours, capacity, and efficiency calculations.
How are productive GPU hours per month calculated?
Productive hours equal scheduled hours per day multiplied by active days per month and utilization divided by 100.
How is monthly workload calculated?
Monthly workload equals the number of GPUs multiplied by throughput per GPU and productive monthly hours.
How is monthly GPU cost calculated?
Monthly GPU cost equals GPU count multiplied by cost per GPU hour and productive monthly hours.
How is B's performance advantage calculated?
The calculator divides B's monthly workload by A's monthly workload, subtracts 1, and multiplies the result by 100.
What does cost per million tasks show?
It estimates the GPU cost needed to complete one million tasks at the entered throughput and hourly rate.
Accuracy and Assumptions
Questions about benchmark consistency and sources of variation in actual results.
Why should both configurations use the same workload?
Matching the model, input data, batch size, precision, software version, and task definition helps ensure that throughput differences reflect the configurations rather than changing test conditions.
Does the calculator account for imperfect multi-GPU scaling?
Only if your entered throughput per GPU already reflects the expected behavior of the configuration. Measure representative scaling where possible.
Does it include cloud storage, networking, and CPU costs?
No. It uses the GPU hourly costs you enter. Other infrastructure and operational costs can be evaluated separately.
Can actual utilization differ from the estimate?
Yes. Queues, data stalls, failures, maintenance, scheduling gaps, and workload changes can affect productive utilization.
Interpreting the Results
Questions about selecting and understanding a GPU comparison result.
Does a positive monthly cost difference mean B is worse?
No. It only means B has a higher estimated GPU cost for the month. B may still provide more capacity or a lower cost per completed task.
What does a negative monthly cost difference mean?
It means B is estimated to cost less than A under the entered schedule and utilization assumptions.
Why might a faster GPU configuration have a worse cost per million tasks?
Its hourly rate may increase by more than its throughput, which raises the estimated cost for each completed task.
Can I use this calculator for on-premises GPUs?
Yes. Use an estimated hourly GPU operating cost based on the cost components you want to include consistently for both configurations.
How is monthly GPU workload calculated?
Monthly workload equals GPU count multiplied by throughput per GPU and productive monthly hours.
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