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 ...