speculative-decoding-draft-model-verify

IN premise — entries/2026/06/21/wiki-LLaMA-chunk-4.md

Created 2026-06-21T09:50:09+00:00

Speculative decoding uses a smaller draft model to propose tokens that a larger model then verifies, accelerating inference by generating multiple tokens per forward pass of the large model.

Summary

Speculative decoding is a trick to make large language models run faster: a small, cheap model guesses the next several words, and the big model checks all of them in a single pass, so you get multiple tokens of output instead of just one per cycle. This matters because it cuts inference cost and latency significantly without changing the final output quality, making it a practical way to scale expensive models to more users.

Dependents

These beliefs depend on this one:

IN inference-optimization-is-multi-layer-discipline — LLM inference optimization operates at multiple layers simultaneously: algorithmic (speculative decoding with draft models), memory management (PagedAttention's KV cache paging), compute (Flash Attention's IO-aware tiling), and caching (KV reuse across tokens).