Design a realistic LLM load test — pick tool (LLMPerf, k6, GenAI-Perf, guidellm), build four patterns (steady, ramp, spike, soak), and gate in CI. Use when you need help with load test plan.
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---
name: load-test-plan
description: Design a realistic LLM load test — pick tool (LLMPerf, k6, GenAI-Perf, guidellm), build four patterns (steady, ramp, spike, soak), and gate in CI. Use when you need help with load test plan.
license: CC-BY-NC-SA-4.0
phase: 17
lesson: 22
metadata:
version: 1.0.0
tags: [load-testing, llmperf, k6, genai-perf, guidellm, llm-locust, ci-gate]
---
Given workload (endpoint, SLA for TTFT/TPOT/error), target scale (concurrency, RPS), and CI posture (PR gate or release-only), produce a load test plan.
Produce:
1. Tool. LLMPerf for baseline runs; k6 + streaming extension for CI gates; GenAI-Perf for NVIDIA-reference runs; guidellm for large synthetic. LLM-Locust only if already on Locust.
2. Prompt distribution. Mean + stddev input tokens from real traffic (if available) or published distribution (ShareGPT / HumanEval). Forbid loop-with-one-prompt.
3. Four patterns. Steady, ramp, spike, soak. For each: target RPS, duration, expected failure mode.
4. CI gate. Specific thresholds: TTFT P95 < X, 5xx < 5%, TPOT < Y. Runtime per PR: 3-5 min.
5. Metric alignment. Note whether the reporting tool is GenAI-Perf-style (ITL excludes TTFT) or LLMPerf-style (ITL includes TTFT). Pick one and stay consistent.
6. Output. A script file (k6 JS, LLMPerf CLI) committed to the repo.
Hard rejects:
- Load test with uniform prompts. Refuse — the numbers lie.
- Load test without streaming support. Refuse — LLM endpoints are streaming by default.
- Comparing numbers across tools without acknowledging metric-definition differences. Refuse.
Refusal rules:
- If the team intends to run on Locust stock without LLM-Locust extension, refuse — GIL trap.
- If CI gate budget is < 60s per PR, refuse full soak — propose a quick steady-state plus separate nightly soak.
- If prompt distribution data is unavailable, require a documented published distribution (ShareGPT) and note the assumption.
Output: a one-page plan with tool, prompt distribution, four patterns with targets, CI gate thresholds, metric alignment. End with the single CI output: PR green only if all thresholds met, 3-run stability.
