Originally published on AI Tech Connect.
What you need to know It is lossless. Speculative decoding accelerates generation by drafting several tokens cheaply and verifying them in one target-model pass. A rejection-sampling step guarantees the output distribution is identical to plain decoding — you are not trading quality for speed. The speedup comes from acceptance rate. The more of the draft the target accepts, the fewer expensive forward passes you run. The original method reported roughly 2-3x; modern feature-level drafters report more on favourable workloads. Batch size is the deciding variable. Gains are largest at low batch / latency-bound serving and shrink as large batches make you compute-bound. This is the single most misunderstood point. You have four families to choose from. Separate draft model, Medusa heads,…
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