Speculative decoding can make LLM answers faster
is a way to make produce answers faster during inference. A small and fast draft model first guesses several upcoming tokens. A larger and slower target model then checks those guesses in parallel.
This lets the system handle more than one token at a time instead of moving strictly one token after another. The goal is faster text generation without lowering the quality of the final answer. SGLang says it reached latency for LLM inference serving by using Modal and Z.ai’s DFlash models.
lists the method, related papers, and later papers that cite the original work.
Key points
- speeds up for .
- A draft model guesses several tokens before the target model checks them.
- The target model verifies guesses in parallel to keep answer quality stable.
- SGLang reports very low latency using Modal and Z.ai’s DFlash models.
- For AI agents, this is most relevant as a possible way to reduce wait time and inference cost.