Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks

arXiv:2605.24217v2 Announce Type: replace Abstract: As Large Language Models (LLMs) transition from research environments to production deployments, evaluating their performance against strict Service Level Objectives (SLOs) has become critical. However, current evaluation methodologies suffer from severe measurement bias at scale. We demonstrate that widely used benchmarking utilities rely on single-process, asyncio-driven architectures that introduce fundamental client-side queuing bottlenecks under high concurrency. By modeling the benchmarking client as an $M/G/1$ queue, we mathematically

Source: arXiv cs.AI — read the full report at the original publisher.

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