Cascade Speculative Drafting for Even Faster LLM Inference
Authors: name: Ziyi Chen, name: Xiaocong Yang, name: Jiacheng Lin, name: Chenkai Sun, name: Jie Huang, name: Kevin Chen-Chuan Chang

Speculative decoding enhances the efficiency of large language models (LLMs)
by leveraging a draft model to draft for a larger target model to review.
However, drafting in speculative decoding involves slow autoregressive
generation and generating tokens of different importance with the same time
allocation. These two inefficiencies lead to its suboptimal performance. To
address this issue, we introduce Cascade Speculative Drafting (CS. Drafting), a
novel approach that employs two types of cascades. The Vertical Cascade
eliminates autoregressive generation from neural models. The Horizontal Cascade
constitutes efficient time allocation in drafting with its optimality supported
by our theoretical analysis. Combining both cascades, our CS. Drafting
algorithm has achieved up to 72 percent additional speedup over speculative
decoding in our experiments while keeping the same output distribution.