Optimizing speculative decoding for LLMs: Drafters and state management

2026-10-09

This technical exploration examines the performance limitations, proposal strategies, and state-commit issues in speculative decoding architectures for large language models. The author reviews techniques including DFlash, DFlash 2, and DSpark, analyzing the interplay between parallel drafting, sequential candidate selection, verification, and recurrent state updates.