Your LLM inference benchmark is lying to you

Most large language model (LLM) inference framework comparisons begin with a leaderboard. One framework posts the highest tokens per second on a standard benchmark, and that number quietly becomes the reason a team adopts it.

The trouble is that the conditions that produce a clean benchmark result rarely resemble the conditions a model faces in production.

Synthetic benchmarks tend to use fixed prompt lengths, steady request rates, and a single model on familiar hardware. Production traffic does none of that.

This article is written for engineering leaders who are choosing an inference framework and want a way to reason about that choice beyond the headline numbers.

It covers why a benchmark winner can underperform once real traffic arrives, three tradeoff axes that usually decide the outcome, and a practical evaluation process you can run before you commit.