Establish a baseline you can trust

Picture it

Imagine trying to tune a race car's suspension while the driver secretly changes the track layout on every lap. If the lap time improves, you have no idea whether your tuning worked or if the track just got shorter. Performance optimization requires locking everything down—the track, the tires, the wind—so that a changing stopwatch strictly measures the single knob you just turned.

What to understand

• Pin model, tokenizer, engine, driver, hardware, and settings
• Warmup, repeats, seeds, correctness, and workload distributions

Follow the references

1. 1 · Documentation

   vLLM · Serving benchmark reference ↗

   Read dataset, request-rate, and output-length options; save the exact command and configuration.

2. 2 · Code + guide

   EleutherAI · Language Model Evaluation Harness ↗

   Choose a small task-specific quality set and retain individual outputs.

Make it concrete

Save a baseline report with hardware, revisions, prompt/output length distributions, arrival process, concurrency, errors, p95 latency, throughput, and quality. Repeat it to estimate run-to-run noise.