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How to chart TTFT and queue depth on a live LLM service with Time to First Token

Connect Prometheus and Grafana to a vLLM server to watch latency and queueing.

Who it is forPlatform or SRE engineer who must explain why a self-hosted model feels slow under load.

The problemAverages hide the latency users feel, so the team needs time to first token and queue depth on a dashboard.

The outcomeA Grafana dashboard showing TTFT, inter-token latency, throughput, and queue depth for your own service.

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. Keep the vLLM server from the first use case running.
  2. Stand up the Prometheus and Grafana stack from the vLLM docs linked in week 3, which requires Docker.
  3. Import the dashboard JSON and confirm the panels move under a light trickle of requests.
  4. Delete any panel whose metric you cannot define, as the README advises.

Prompt for your agent

In the patchy631/time-to-first-token repo, set up the Prometheus and Grafana stack against a running vLLM server at <server URL>. Expected output: a Grafana dashboard with live TTFT and queue depth panels. Verify that the TTFT panel and the waiting-requests metric change after you send 20 test requests.

Caveat. The dashboard setup follows the linked vLLM docs, so metric names may change between vLLM releases.

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