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How to load test an LLM endpoint past 1000 concurrent requests with Time to First Token

Use guidellm to sweep concurrency and report p50, p95, and p99 latency.

Who it is forPerformance engineer preparing an LLM service for peak traffic.

The problemSingle-request tests miss saturation, so a service can look healthy and still fail under real concurrency.

The outcomeA reproducible concurrency sweep with p50, p95, and p99 output, cross-checked by a second tool.

What you need first

  • Python and command-line comfort
  • One rented GPU with about 24GB of memory for a 7-8B model, billed by the half hour
  • Docker for the Prometheus and Grafana stack
  • An H100 for the 1000-concurrent load test in week 5

Steps

  1. pip install guidellm
  2. Run a guidellm concurrency sweep against your endpoint, stepping up to 1000 or more concurrent requests. (inferred, check the README)
  3. Record p50, p95, and p99 latency at each level, and discard the saturation point from the summary as the README's benchmarking reading recommends.
  4. Run a second load tool against the same endpoint and compare the results.

Prompt for your agent

Using the patchy631/time-to-first-token repo, run a guidellm concurrency sweep against the endpoint at <server URL> up to 1000 concurrent requests. Expected output: p50, p95, and p99 latency at each concurrency level. Verify that the results from a second load tool agree with guidellm within a reasonable margin.

Caveat. The README uses one H100 for this session, so budget for that GPU cost on top of the smaller rental used earlier.

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