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A/B Testing GPU Performance (Monthly) Calculator

Compare two GPU configurations by estimating their monthly workload capacity, operating cost, and cost efficiency.

Your Details

Overview

Use this monthly GPU A/B testing calculator to compare two GPU configurations using their GPU count, measured throughput, hourly cost, planned run time, and expected utilization. It estimates monthly task capacity and the cost efficiency of each option.

How it works

The calculator first estimates productive monthly hours by multiplying scheduled hours, active days, and utilization. It multiplies those hours by the GPU count and benchmark throughput to estimate each configuration's monthly completed tasks. Monthly GPU cost is calculated from GPU count, hourly price, and productive hours. Cost per million tasks then makes it easier to compare efficiency even when the two configurations have different throughput or pricing.

How to use this calculator

  1. 1Enter the number of GPUs in configuration A and configuration B.
  2. 2Add the benchmark throughput per GPU for the same workload and test conditions.
  3. 3Enter the hourly cost for each GPU configuration.
  4. 4Set the expected daily schedule, active days per month, and productive utilization.
  5. 5Compare monthly output, cost per million tasks, and the performance difference.

Example Calculation

GPUs in configuration A

4

Configuration A throughput per GPU

120

Configuration A cost per GPU hour

$2

GPUs in configuration B

4

Configuration B throughput per GPU

180

Configuration B cost per GPU hour

$3

Scheduled hours per day

8

Active days per month

22

Expected GPU utilization

80%

Configuration B performance advantage

50.0%

With 140.8 productive hours per month, configuration A completes about 67,584 tasks and configuration B completes about 101,376 tasks. Configuration B provides about 50% more throughput, while its estimated cost per million tasks is lower.

Frequently asked questions

What should I use for GPU throughput?

Use a benchmark result measured in tasks per hour per GPU, such as generated images, processed records, inference requests, training steps, or another consistent unit for your workload.

Why must both GPU tests use the same workload?

A valid comparison needs matching model versions, input data, batch size, precision, software settings, and performance metric. Changing these conditions can make throughput results misleading.

What does expected GPU utilization mean?

It is the percentage of scheduled time during which GPUs are expected to do productive work. It can account for queue time, data loading, maintenance, gaps between jobs, and idle capacity.

Does the calculator include all infrastructure costs?

No. It uses the GPU hourly costs you enter. Storage, data transfer, CPU resources, licensing, support, and engineering time should be assessed separately if relevant.

Why can a more expensive GPU setup have a lower cost per task?

A higher hourly price can be offset by substantially greater throughput. Cost per million tasks compares cost with completed work rather than price alone.

Can I use this calculator for cloud GPUs and on-premises hardware?

Yes. For cloud GPUs, enter the hourly rental price. For on-premises hardware, use an estimated hourly operating cost that reflects the costs you want to compare.

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Assumptions and warnings

Assumptions

  • Both configurations run the same workload, model settings, batch size, precision, and data pipeline.
  • Measured throughput per GPU remains broadly consistent throughout productive running time.
  • GPU utilization represents productive compute time and is applied equally to both configurations.
  • Hourly rates include the GPU cost entered but may exclude storage, networking, CPU, engineering time, and other platform charges.
  • Results are estimates based on the values entered and do not account for availability interruptions or workload variability.

Warnings

  • This calculator provides an operational cost and performance estimate only; actual cloud bills and benchmark results can differ.
  • Test both configurations under representative production conditions before committing to a major infrastructure decision.