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What you can do with Time to First Token

A 10-week self-study roadmap for running LLM inference serving in production, built around one OpenAI-compatible service that gets deployed, instrumented, load tested, optimized, and benchmarked.

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patchy631/time-to-first-token on GitHub

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What do you want to do?

4 use cases

  • Serve a 7-8B model behind an OpenAI-compatible endpoint

    Deploy vLLM on one rented 24GB GPU and call it with OpenAI-style requests.

    View prompt
    Using the patchy631/time-to-first-token repo as the guide, set up vLLM on <GPU host> serving Qwen2.5-7B behind an OpenAI-compatible API. Expected output: a chat completion response from the endpoint. Verify that the response contains a non-empty assistant message.

  • Chart TTFT and queue depth on a live LLM service

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

    View prompt
    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.

  • Compare vLLM and SGLang on a shared-prefix workload

    Run one model on both engines to measure the TTFT gap on prefix-heavy traffic.

    View prompt
    Using the patchy631/time-to-first-token repo, run the same model with a shared system prompt on vLLM and on SGLang at <GPU host>. Expected output: a table of TTFT and throughput for each engine. Verify that both runs used the same model, prompt set, and hardware before comparing numbers.

  • Load test an LLM endpoint past 1000 concurrent requests

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

    View prompt
    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.

How to use Time to First Token

  1. Check you have what it needs: 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.
  2. Open patchy631/time-to-first-token on GitHub and read the README and license.
  3. Rent one 24GB GPU by the half hour, for example on RunPod or Modal.
  4. pip install vllm
  5. Start the server with vllm serve meta-llama/Llama-3.1-8B-Instruct, using vLLM's OpenAI-compatible server as the README describes. (inferred, check the README)
  6. Send one chat request to the endpoint from an OpenAI-compatible client and confirm a response comes back.

These steps come from our top use case, Serve a 7-8B model behind an OpenAI-compatible endpoint, which also has a copy-paste agent prompt.

Time to First Token alternatives · Best AI repos for Software and DevOps

About this repo

Requirements

  • 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
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Questions about Time to First Token

What is Time to First Token?

A 10-week self-study roadmap for running LLM inference serving in production, built around one OpenAI-compatible service that gets deployed, instrumented, load tested, optimized, and benchmarked. It is an open-source project on GitHub: https://github.com/patchy631/time-to-first-token.

Who is Time to First Token for?

Engineers who know Python, transformers at the architecture level, and the command line, and want hands-on LLM serving experience on rented GPUs.

What can I do with Time to First Token?

4 use cases so far, for example: Serve a 7-8B model behind an OpenAI-compatible endpoint; Chart TTFT and queue depth on a live LLM service; Compare vLLM and SGLang on a shared-prefix workload. Each has steps and a copy-paste agent prompt.

What do I need to run Time to First Token?

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.

What are alternatives to Time to First Token?

Other hot AI repos in Software and DevOps: Harness2, iFixAi, Orca, Diagram Design, AI Memory.

Is Time to First Token safe to run?

Deduped does not own, audit or vouch for it. Read the README and license, run it on your own systems first and review every command before you run it.