Are LLM Performance Benchmarks Reliable?

Official Schedule Context

Standardizing performance benchmarks for production-grade Large Language Models is currently a

significant challenge across the industry. Conflicting data is prevalent, whether originating from

server developers like vLLM and SGLang or from various analysts and competitive benchmarks, and

these results often fail to hold up under real-world conditions. Our research into these

inconsistencies identified several critical factors, including the constraints of single-process

tools, specifically the Python Global Interpreter Lock (GIL) and the nuances of model-level settings

like temperature. Furthermore, a lack of transparency regarding load generation parameters such as

QPS and concurrency, paired with insufficient observability into the benchmarking clients

themselves, contributes to these disparate outcomes. In this talk, we share key lessons learned from

our benchmarking efforts, examining the primary pitfalls that distort performance data and offering

strategies for mitigation. Additionally, we will introduce Inference Perf, an open-source, multi-

process utility we developed to provide reliable stress-testing for production stacks. Our goal is

to promote standardized, real-world benchmarking practices that allow the community to move beyond

unreliable data. Join us to discover how to accurately measure, optimize, and report LLM performance

with certainty.

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