Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks
A paper on arXiv (2605.24217) reveals that common LLM benchmarking tools introduce severe measurement bias due to single-process, asyncio-driven architectures. By modeling the client as an M/G/1 queue, the authors show that Python's Global Interpreter Lock (GIL) artificially inflates Time to First Token (TTFT) and Time Per Output Token (TPOT) as request rates scale. To address this, they propose an unbiased multi-process evaluation framework that distributes client-side load, eliminating queuing overhead. They also introduce Normalized Time Per Output Token (NTPOT), a composite metric that amortizes end-to-end latency including prefill and scheduling delays across sequence lengths. Empirical results demonstrate that the new methodology provides more accurate performance measurements for production deployments.
Developers relying on current benchmarks may overestimate latency, leading to incorrect SLOs and resource allocation.