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AI Research
arXiv CS.AI·May 26·5 min read
Identifying and Mitigating Systemic Measurement Bias in Production LLM Inference Benchmarks
TL;DR
Current benchmarking methods for Large Language Models (LLMs) suffer from measurement bias due to client-side queuing issues. A new multi-process evaluation framework was developed to distribute load and mitigate these biases.
✦ Why It Matters
Engineers can adopt the multi-process framework to achieve more accurate LLM performance evaluations in production settings.
Key Takeaways
- Adopt a multi-process architecture for LLM benchmarking.
- Use Normalized Time Per Output Token for accurate latency measurement.
- Model client-side load to identify queuing bottlenecks.
- Evaluate LLMs at scales exceeding thousands of queries per second.
How It Works
The proposed multi-process evaluation framework distributes the load across multiple processes, reducing the impact of queuing delays caused by the Python GIL. By employing a mathematical model of the benchmarking client as an M/G/1 queue, the framework effectively isolates the performance of the serving engine from client-side bottlenecks.
The introduction of the NTPOT metric allows for a comprehensive assessment of latency, including prefill and scheduling delays, across varying sequence lengths.