How to Benchmark Throughput Before You Need the Capacity

• Input and output sequence length: a benchmark run on short prompts and short completions will look nothing like your production numbers if your real traffic runs long context.
• Batch size and concurrency: throughput at a batch size nobody will actually run in production tells you about a scenario you won't see.
• Hardware and quantization: the exact GPU type and precision used, since the same model can post very different numbers across configurations.
• Whether the benchmark includes real-world overhead: tokenization, network hops, and queueing, or just raw model execution in isolation.

A number missing any of these is a marketing figure, not an engineering one.

Pull a sample of real production requests, or realistic synthetic ones if you're pre-launch, and use their actual sequence length distribution as your benchmark input, not a fixed short prompt that's easy to test with. Run the benchmark at the batch sizes and concurrency levels you'll realistically hit at peak, not just at whatever setting produces the best headline number.

Report a distribution, not a single number: throughput and latency at your typical load, and again at your expected peak, since the gap between the two is often where capacity planning actually goes wrong.

A common mistake is benchmarking once with the average prompt and concluding there is plenty of room, then discovering that a small share of very long requests dominates GPU time in production. The fix is to include the long tail on purpose. Take your longest realistic requests, mix them into the benchmark at roughly the frequency you see them, and report results separately for typical and long cases. If the long cases behave very differently, that is a capacity finding worth acting on, even when the average looks comfortable.

Say you're launching a feature expected to bring your peak concurrent requests to roughly triple current levels. Benchmark your current setup at that multiple of load using realistic prompt lengths, not the average, since a launch typically shifts the traffic mix toward more complex requests, not fewer.

If throughput at that load falls short of what you need, you have two real levers: more replicas, or a configuration change like a larger batch window, and the benchmark should tell you which lever actually closes the gap before the launch, not after it.

• Benchmarking with short, simple prompts because they're convenient, when production traffic runs long and complex.
• Testing throughput without testing latency at the same load, missing that throughput can hold while individual requests slow down badly.
• Running the benchmark once before launch and never again, missing how usage patterns shift as the product actually gets used.

A benchmark report that's just a table of numbers gets forgotten within a quarter. Attach the conditions, sequence lengths, batch size, hardware, directly to the numbers, and note the specific decision the benchmark was meant to inform, whether current capacity covers an upcoming launch, for instance.

A number without its conditions ages badly: months later, nobody remembers whether it was measured at typical or peak load, and the report becomes something people cite without being able to defend.

Re-run it whenever a major traffic driver changes: a new feature likely to shift usage patterns, a model swap, or a meaningful jump in user count. Outside of those triggers, a quarterly check against current traffic is a reasonable default for most small and mid-sized teams.

A benchmark from launch tells you nothing reliable about capacity a year later if your product and its usage have changed underneath it, which they almost always have, sometimes in ways nobody on the current team remembers planning for. Treat the re-benchmark trigger list as a living checklist attached to your launch and roadmap process, not a calendar reminder someone eventually snoozes into irrelevance.