Roofline Model for Estimating Speculative Decoding Speedup in LLM Inference
Summary
This article presents a roofline model for estimating speedup ratios from speculative decoding in large language model (LLM) inference. It analyzes how different draft lengths, acceptance probabilities, block sizes, and relative costs per token/block affect the speedup achieved across various models, hardware configurations, batch sizes, and sequence lengths. The model shows speedup ranging from 1.0x (at γ*=0) to 1.6x (at γ*=16 max), with acceptance probabilities of 75-89% and relative costs around 10%. The author notes this is only a modeling tool that tends to underestimate benefits when overhead is a major latency contributor.
Source
Twitter / XRoofline Model for Estimating Speculative Decoding Speedup in LLM Inferencemodal.comKey quotes
· 5 pulledγ*=0, 1.0x speedup
γ*=16 (max), 1.6x speedup
This modeling system uses roofline analysis to estimate the speedups from speculative decoding for different draft lengths
It is only a model!
It tends to underestimate the benefit when overhead is a major contributor to latency, e.g. small batch sizes on small models
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