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Compare the same useful output under documented conditions; offloading, precision and compilation are part of the tested configuration.
A Stable Diffusion GPU benchmark needs the same model, resolution, step count, generation settings and software environment. Compare complete-image time, batch throughput and peak memory. A number copied from another table without those conditions does not determine performance for your workflow or justify a hardware purchase.
Create an experiment record
Save model and component revisions, library version, scheduler, prompt, seed, image dimensions, batch and relevant numeric settings. Diffusers documents how precision, attention implementations and compilation can affect execution. These choices are part of the comparison, not incidental details around two GPU names.
Check that outputs complete and meet the intended quality before ranking timing results. If one configuration changes precision or uses different components to fit, label it as a distinct complete setup. That can be a useful practical comparison, but it does not isolate the GPU alone.
Separate timing boundaries
| Measurement | What to include | Question answered |
|---|---|---|
| First startup | Preparation and loading | How quickly can work begin from scratch? |
| Repeated pass | Ready environment and a new task | What is sustained execution like? |
| Complete batch | All images and overhead | How does the queue complete? |
| Time to result | Waiting and execution | What does the user experience? |
State the timer boundaries so another operator can reproduce them. Do not compare one system’s warm sampling interval against another system’s cold start and call the difference a general acceleration factor.
Use several relevant scenarios
Include a standard job, a larger permitted resolution and the batch pattern the application needs. Keep these results separate instead of combining them into one headline rate. Record failed jobs and actual completed outputs so the best successful case does not conceal an unreliable boundary.
Repeat the representative test to inspect stability under documented background conditions. Watch RAM, storage and GPU memory as well as elapsed time. A neighboring process or model-loading change can explain a difference that would otherwise be attributed to the card.
Account for offloading and useful cost
When one card needs offloading, measure the entire pass and host resources. GPU-only timing would omit part of its work. Preserve the memory method and inspect the saved image, especially after precision or placement changes.
Use measured complete output counts and explicit operating-cost assumptions when discussing economics. No universal image price follows from the benchmark alone. For operations funding, the useful evidence is a reproducible task and resource profile rather than an undocumented ranking or promised return.
Official references: huggingface.co — fp16, huggingface.co — memory.
Related guides: Stable Diffusion GPU requirements: resolution, batch and weights, LoRA and QLoRA: understanding fine-tuning memory.