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Speculative decoding: measure when it helps

Benchmark vLLM prompt-lookup speculation against ordinary decoding. Check acceptance, output correctness, latency and goodput with matched GPU runs.

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AI Infra Interviews editorial · Updated

How do you measure whether speculative decoding helps an inference workload?

Run ordinary and speculative decoding on the same model revision, requests, sampling settings and concurrency. Count accepted draft tokens separately from complete output, and retain incorrect or failed answers when calculating useful throughput. Repeat paired runs in both orders, check client latency and compare memory observations at a defined boundary. Prompt lookup provides a first experiment without a separate draft model. Its acceptance rate alone cannot establish a speedup, and copied passages cannot stand in for every production task.

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What you build
Paired GPU request records and a decision report that separates accepted drafts, complete output and correct answers.
Environment
Linux · Python 3.12 · One NVIDIA GPU
Plan your session
2–3 hours plus environment setup. A planning estimate, not measured runtime.
Before you begin
Complete the free streaming client project and understand its success checks. Use a single-GPU Linux host with the pinned engine environment, sufficient device memory and disk space for the small model.
Paired GPU experiment executed. 384 measured GPU requests across three paired blocks, two workloads and two client counts, plus 96 warmups. Copying passed the declared checks; transformation tasks failed the quality threshold. Prompt-lookup results on a small target do not establish learned-draft performance, stochastic distribution preservation or production saturation.
  1. 01Freeze the experiment

    Keep the model, cases, sampling and serving settings identical across arms.

  2. 02Measure proposals and answers

    Reconcile engine counters with complete client request journals.

  3. 03Defend the decision

    Compare paired timings only after checking correctness and service limits.

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

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Premium includes this guide’s complete walkthrough and downloadable project files. The technical overview, prerequisites and verification notes above are public.

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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.