Speculative Decoding
A speed optimization where a small, fast 'draft' model generates candidate tokens that a larger model then verifies in parallel. Achieves near-large-model quality at small-model speeds — 2-3x faster generation.
Why it matters
Large models are slow because they generate one token at a time. Speculative decoding speeds this up by letting a small, fast model guess several tokens ahead, which the big model then checks all at once. When the guesses are right, you skip a lot of waiting. It's a clever trick that makes powerful models feel snappier without lowering the quality of the final output.
In practice
Picture a fast intern drafting the next few words of a sentence and a careful editor reviewing them in one pass. If the editor agrees, those words are accepted instantly instead of being written slowly one by one; if not, the editor corrects them. That's speculative decoding: the small draft model proposes, the large model verifies, and correct guesses let responses come out two to three times faster.
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