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Tune vLLM serving parameters with controlled experiments

Compare token budgets or sequence limits with matched workloads, all-attempt goodput and repeated paired runs. Includes a small-model RTX 5070 branch.

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AI Infra Interviews editorial · Updated

How do you tune vLLM without mistaking noise for improvement?

Change one serving parameter while fixing the model revision, runtime, workload and client settings. Compare token budgets or sequence limits in repeated paired blocks and count only complete, correct answers within both latency limits as useful work. This project includes an executed small-model GPU branch; its four synthetic cases teach measurement mechanics, not application quality or production capacity.

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What you build
Recorded serving configurations, raw request evidence and a comparison that rejects mismatched experiments.
Environment
Python 3.12 client · an existing single-GPU vLLM host
Plan your session
2–4 hours plus measured GPU runs. A planning estimate, not measured runtime.
Before you begin
Complete the streaming client lab and keep its two Python files. Use either the pinned Qwen3.5/L4 deployment or the separate Qwen3-0.6B local GPU branch.
Small-model GPU branch executed · L4 branch pending. Qwen3-0.6B served on RTX 5070 with vLLM 0.31.0. Complete responses can still fail exact task checks. The original Qwen3.5/L4 branch remains source-reviewed and unexecuted.
  1. 01Hold inputs fixed

    Hash the cases, client and declared server settings.

  2. 02Change one setting

    Compare token budgets or sequence limits separately and retain all attempts.

  3. 03Compare paired blocks

    Check acceptable answers per second alongside the joint pass fraction.

Follow the changing state through the project. Each boundary has a check in the walkthrough.

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Start with Stream vLLM responses with Python and detect incomplete answers (Free to read).

Primary sources

Checked . Source review and execution checks are described separately above.