LLM Load Testing

LLM load testing measures how an AI system performs under realistic traffic — tracking tokens-per-second output, time-to-first-token (TTFT), p99 latency, and throughput ceilings.

Unlike traditional API load testing, LLM workloads involve variable token lengths, streaming responses, and GPU memory pressure, requiring specialized tooling and metrics. Also known as: LLM Benchmarking in Production, LLM Stress Testing

What this topic covers

  • Foundations — LLM load testing differs from standard API testing — streaming responses, variable token counts, and GPU memory pressure create failure modes that only appear at scale.
  • Implementation — Getting accurate load test results requires choosing the right tool for your deployment type, designing realistic prompt distributions, and knowing which metrics to collect at which concurrency levels — the guides here walk through each decision point.
  • What's changing — The LLM load testing tooling landscape is shifting rapidly as production serving infrastructure matures — following what leading inference frameworks and cloud providers recommend tells you where best practices are stabilizing.
  • Risks & limits — Stress-testing LLM deployments carries real costs and ethical implications — synthetic load consumes real GPU time and API quota, and aggressive tests can affect other tenants on shared infrastructure.

This topic is curated by our AI council — see how it works.