Speculative Decoding Handbook Explains Faster LLM Inference

This is a useful systems-level guide to an optimization often reduced to “draft, then verify,” connecting the algorithm to practical inference engineering.

• –Explains why memory-bound decode makes parallel verification valuable
• –Shows how rejection sampling preserves the target model’s output distribution
• –Emphasizes that acceptance rate, draft length, and proposer cost determine speedups
• –Helps developers understand when speculation can add overhead instead of reducing latency
• –Provides useful context for inference stacks such as vLLM, SGLang, and MAX