A/B Testing GPU Performance Per-User Formula
Learn how to calculate GPU time, infrastructure cost, and estimated savings per user when comparing an A/B test control with a variant.
This calculator estimates how much GPU processing time and effective infrastructure cost are attributable to one user over a selected period. It compares a control and variant using average GPU time per request, request volume, and hourly GPU cost, then scales the per-user cost difference to the expected user count.
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Estimated Period GPU Cost Savings
Where:
Calculate each group's GPU time per user, convert seconds to GPU hours, multiply by the relevant hourly GPU cost, find the per-user cost difference, and multiply it by the number of users.
Variables Explained
| Variable | What It Means | Unit |
|---|---|---|
| controlGpuSecondsPerRequest - Control GPU time per request | Average GPU processing time for one GPU-backed control request. | seconds |
| variantGpuSecondsPerRequest - Variant GPU time per request | Average GPU processing time for one GPU-backed variant request. | seconds |
| requestsPerUser - GPU requests per user | Average number of GPU-backed requests made by one user in the chosen period. | number |
| controlGpuHourlyCost - Control GPU cost per hour | Effective hourly GPU infrastructure cost for the control configuration. | currency |
| variantGpuHourlyCost - Variant GPU cost per hour | Effective hourly GPU infrastructure cost for the variant configuration. | currency |
| usersPerPeriod - Users in period | Number of users expected to use the GPU-backed feature in the same period. | number |
Step-by-Step Calculation
Calculate control GPU time per user
Multiply average control GPU seconds per request by average requests per user.
controlGpuSecondsPerUser = controlGpuSecondsPerRequest * requestsPerUser
Calculate variant GPU time per user
Use the same request volume to estimate the variant's GPU seconds per user.
variantGpuSecondsPerUser = variantGpuSecondsPerRequest * requestsPerUser
Find time saved per user
A positive result means the variant uses fewer GPU seconds per user.
gpuTimeSavedPerUser = controlGpuSecondsPerUser - variantGpuSecondsPerUser
Calculate percentage time reduction
Express the saved GPU time as a percentage of control GPU time.
gpuTimeReduction = (gpuTimeSavedPerUser / controlGpuSecondsPerUser) * 100
Convert GPU seconds to GPU hours
Hourly infrastructure costs require GPU usage to be expressed in hours.
gpuHoursPerUser = gpuSecondsPerUser / 3600
Calculate cost savings per user
Compare estimated control and variant GPU cost for one user.
costSavingsPerUser = (controlGpuHoursPerUser * controlGpuHourlyCost) - (variantGpuHoursPerUser * variantGpuHourlyCost)
Scale savings to the period
Apply the estimated per-user difference to all users in the selected period.
periodCostSavings = costSavingsPerUser * usersPerPeriod
Example: Faster inference variant with the same GPU hourly rate
Control GPU time per user
2.5 × 120
300 GPU seconds
Variant GPU time per user
1.8 × 120
216 GPU seconds
GPU time saved per user
300 − 216
84 GPU seconds
GPU time reduction
84 ÷ 300 × 100
28%
Control cost per user
300 ÷ 3600 × $2.50
$0.2083
Variant cost per user
216 ÷ 3600 × $2.50
$0.1500
Period cost savings
($0.2083 − $0.1500) × 10,000
$583.33
Final Result
The variant saves 84 GPU seconds per user, reduces GPU time by 28%, and estimates about $0.0583 saved per user or $583.33 across 10,000 users.
Assumptions
- ✓Average GPU time per request represents each experiment group for the selected period.
- ✓Each user makes the entered average number of GPU-backed requests.
- ✓Hourly GPU cost is a consistent effective cost measure for each configuration.
- ✓Per-user time and cost scale linearly across the entered user count.
- ✓The control GPU time per user is greater than zero when calculating percentage reduction.
Limitations
- !Average GPU time can conceal queueing, batching, cold starts, retries, and long-tail latency.
- !GPU time alone does not measure output quality, reliability, throughput, or user satisfaction.
- !Effective hourly cost may include fixed capacity that does not decrease immediately when GPU seconds fall.
- !Traffic mix and request complexity can differ between users and experiment groups.
- !The calculation does not test statistical significance or establish causation.
Common Mistakes to Avoid
Entering wall-clock request latency instead of actual GPU processing time when those measures differ.
Using different reporting periods for requests per user and users in period.
Comparing a shared GPU's list price with a fully allocated cost for the other group.
Treating a positive time saving as a guaranteed cash saving despite fixed or reserved capacity.
Ignoring a higher variant GPU hourly rate when the variant uses a different hardware configuration.
Related Formulas
Frequently Asked Questions
How is GPU time per user calculated?
GPU time per user equals average GPU seconds per request multiplied by average GPU-backed requests per user in the selected period.
How is GPU time reduction calculated?
The calculator divides GPU time saved per user by control GPU time per user and multiplies the result by 100.
What does a negative GPU time saving mean?
It means the variant uses more GPU processing time per user than the control under the entered assumptions.
Can a faster variant cost more per user?
Yes. Its hourly GPU cost can be high enough to outweigh the reduction in GPU time.
Why does the formula divide by 3,600?
There are 3,600 seconds in an hour, so the conversion is needed before applying an hourly GPU cost.
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