Co-Designing AI Models Using Speculative Decoding for Faster LLM Inference
This article is the third in the series on AI model collaborative design. It explores how to use speculative decoding to accelerate large language model inference while maintaining accuracy. The article provides five guidelines for selecting the optimal draft length and draft mechanism at the Pareto frontier. Related discussions indicate that model design choices affect throughput and interactivity without sacrificing accuracy.