Load Testing LLM APIs — Why k6 and Locust Lie
Traditional load testers were not designed for streaming responses, variable output lengths, token-level metrics, or GPU saturation. Two traps bite most teams. The GIL trap: Locust's token-level measurement runs tokenization under the Python GIL, which competes with request generation under heavy concurrency; tokenization backlog then inflates reported inter-token latency — your client is the bottleneck, not the server. The prompt-uniformity trap: identical prompts in a loop test one point on the token distribution; real traffic has variable length and diverse prefix matches. LLMPerf fixes this with --mean-input-tokens + --stddev-input-tokens. Tool mapping in 2026: LLM-specialized (GenAI-Perf, LLMPerf, LLM-Locust, guidellm) for token-level accuracy; k6 v2026.1.0 + k6 Operator 1.0 GA (Sept 2025) — streaming-aware, Kubernetes-native distributed via TestRun/PrivateLoadZone CRDs, best for CI/CD gates; Vegeta for Go constant-rate saturation; Locust 2.43.3 only with LLM-Locust extension for streaming. Load patterns: steady-state, ramp, spike (autoscaling test), soak (memory leaks). Explain the two anti-patterns (GIL trap, prompt-uniformity trap) that make generic load testers lie for LLM APIs. Pick a tool for a given purpose: LLMPerf (benchmark run), k6 + streaming extension (CI gate), guidellm (large-scale synthetic), GenAI-Perf (NVIDIA reference). Design four load patterns (steady, ramp, spike, soak) and name the failure mode each catches. Build a realistic prompt distribution using mean + stddev of input tokens rather than fixed length. You k6-tested your LLM endpoint at…
Load Testing LLM APIs — Why k6 and Locust Lie: Traditional load testers were not designed for streaming responses, variable output lengths, token-level…
This free lesson is part of the AI Engineering from Scratch curriculum. Read the full explanation, run the lesson code, and verify the result in the interactive reader or from the repository source.
Browse the complete course catalog or open this lesson on GitHub.