How to Load-Test a Model Endpoint Without Faking the Results

• Vary sequence length the way production actually does, not a fixed short prompt repeated at volume.
• Vary request timing, including bursts, since steady, evenly-spaced synthetic load misses how real traffic actually arrives.
• Include a realistic mix of request types, if your endpoint handles more than one kind, weighted by actual frequency.
• Ramp gradually and also test a sudden spike, since these two patterns stress different parts of your system.

A test that only covers one of these dimensions will pass while missing exactly the condition that causes a real incident.

A load test that reports only whether requests eventually succeeded misses the more useful signal: how latency and error rate change as load increases. Watch for the point where latency starts degrading faster than load is increasing, since that inflection point is your real capacity ceiling, not the point where requests start failing outright.

Also watch GPU memory pressure and queue depth during the test, not just after it. A system that recovers cleanly once load drops can still have spent the test period in a degraded state that a pass or fail summary won't show you.

Say a load test ramps traffic steadily and requests keep succeeding all the way to your target load, technically a pass. Looking at the latency curve underneath that pass shows response times tripling over the last third of the ramp, well before any requests actually failed.

That's the real finding: your system degrades gracefully enough to avoid outright failures, but users at that load are having a noticeably worse experience than the pass or fail result suggests. Treat that inflection point, not the failure point, as your actual capacity limit for planning purposes.

Load testing answers whether you can handle expected traffic. Stress testing deliberately goes past that, well beyond any traffic you expect, to find out how the system fails: gracefully, with clear errors and a recovery once load drops, or badly, with cascading failures that outlast the traffic spike itself.

Run this in a lower environment specifically, since finding your breaking point in production is an incident, not a test, and treat whatever you learn from it as an input to the runbook you'd use if it happened for real.

Before any run, write down the question the test should answer and the result that would change a decision. For example: can we absorb a launch that triples peak traffic without latency crossing the level customers notice, and if not, do we add replicas or change batching? A test framed that way produces a finding someone can act on. A test run just to see what happens produces a chart that gets shared once and forgotten. Keep the scenario definition in your repository so the same test can be repeated after every significant change.

Finding where the system breaks is only half the test. Once you've pushed past capacity, ease load back down and watch whether the system actually recovers cleanly on its own, queue depth draining, latency returning to normal, or whether it stays degraded even after the pressure is gone.

A system that recovers slowly, or needs a manual restart to clear a backed-up queue, has a different operational risk than one that degrades gracefully and bounces back the moment load drops. Both can pass a simple breaking-point test; only testing recovery tells them apart, and that distinction is exactly what determines how bad a real spike ends up being.

• Testing with uniform, short prompts because they're easy to generate, when real traffic varies widely in length.
• Treating a pass or fail result as the whole picture, missing a degrading latency curve underneath a technically passing test.
• Never testing a sudden spike, only a gradual ramp, when real incidents often start with a spike.