Red Hat Developer published a technical guide on optimizing vLLM speculative decoding using FastMTP heads1. The approach builds on multi-token prediction (MTP), a training objective used by the DeepSeek and Qwen model families that learns to predict several future tokens at each position, improving data efficiency and quality. Standard autoregressive decoding is memory-bandwidth bound, with hardware spending most of its time moving weights rather than computing during each full forward pass.
Red Hat Details FastMTP Heads to Speed vLLM Speculative Decoding
Red Hat Developer published a guide on using FastMTP heads to optimize vLLM speculative decoding, leveraging multi-token prediction from DeepSeek and…