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vLLM turns on async scheduling by default. What does it overlap, what does it need from the model runner, and when must it be off?

Scheduling step n+1 before step n's tokens exist sounds impossible. The answer shows the placeholder trick that makes it work, what it costs at an end-of-sequence token, and the configurations where vLLM switches it off for you.

Updated Oct 2026 · Worked answers on inference mechanisms, with calculations, implementation details, and follow-up questions.

Scheduling step n+1 before step n's tokens exist sounds impossible. The answer shows the placeholder trick that makes it work, what it costs at an end-of-sequence token, and the configurations where vLLM switches it off for you.

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Next in this trackDesign a batch inference service for 10 billion tokens a day on a 70B model, optimizing for cost rather than latency.Next in this trackSpeculative decoding inside a continuous batch: how does the scheduler budget draft tokens, and what happens to the KV on rejection?Next in this trackTraffic triples for a minute on your LLM API. What do you reject, at what point, and what do you tell the client?
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