Tuning Speculative Decoding Under a Fixed Verification Budget
You are deploying speculative decoding for a customer-support chat product. Each generation cycle works as follows: (1) a small, fast draft model autoregressively proposes a block of τ candidate tokens; (2) a large verification model evaluates all τ candidates in one parallel forward pass; (3) you append only the maximum consecutively accepted prefix of the draft block (stop at the first rejected token), and then the verification model generates the next token after that accepted prefix before starting the next cycle.
Your platform team imposes a hard budget: you may run at most 1 verification-model forward pass per cycle, and the verification model is the dominant cost. You can choose between two draft models:
- Draft A: very fast but less accurate (tends to have an early rejection in the block).
- Draft B: slower but more accurate (tends to have longer consecutively accepted prefixes).
Write an evaluation recommending which draft model you would choose and how you would set τ to maximize end-to-end throughput while keeping output quality identical to the verification model alone. Your answer must explicitly connect (a) the roles of the draft vs verification model, (b) why parallel verification is the main speedup lever, and (c) how the “maximum consecutively accepted tokens” rule changes the tradeoff between draft accuracy and τ (including what happens when the first rejection occurs early vs late in the block).
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Ch.5 Inference - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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