Speculation Is All You Need | Modal Blog

What this post added

This post introduces and advocates for speculative decoding as a primary engine optimization for LLM inference. It details the mechanism of speculative decoding, explaining how a draft model generates token sequences that are then validated by the target model, leading to lossless acceleration. The post highlights the importance of acceptance length and demonstrates how training custom speculators, particularly using architectures like DFlash, can yield significant speedups (2x-3x). It also emphasizes that speculator training is a simplified ML problem due to readily available data and a clear objective metric, and showcases how Modal's infrastructure accelerates this training process, enabling application-specific optimizations.