Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks
For engineers deploying LLMs in production, this work addresses a critical measurement bias that undermines the reliability of performance benchmarks against SLOs.
The paper identifies that current LLM inference benchmarks suffer from systemic measurement bias due to client-side queuing bottlenecks caused by Python's GIL, and proposes a multi-process evaluation framework and a composite metric (NTPOT) to enable accurate, reproducible profiling at production scales exceeding thousands of queries per second.
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 demonstrate how the Python Global Interpreter Lock (GIL) artificially inflates Time to First Token (TTFT) and Time Per Output Token (TPOT) metrics as request rates scale. To resolve this systematic inaccuracy, we propose an unbiased, multi-process evaluation framework that effectively distributes client-side load, ensuring negligible queuing overhead. Furthermore, we formalize a composite metric, Normalized Time Per Output Token (NTPOT), to robustly amortize end-to-end latency, including prefill and scheduling delays across sequence lengths. Our empirical evaluation demonstrates that this methodology successfully isolates pure serving engine performance, enabling accurate, reproducible profiling of LLMs at production scales exceeding thousands of queries per second.