Speculation is all you need: Intro to Speculative Decoding for High Performance Inference

Speculative decoding optimizes large language model (LLM) inference by employing a smaller, faster draft model to propose token sequences that a larger target model verifies in parallel. This technique exploits spare GPU compute capacity to generate multiple tokens per forward pass, significantly reducing inter-token latency. Advanced architectures like Eagle and DFlash enhance this process by integrating hidden states from the target model to improve proposal accuracy, with DFlash enabling efficient block-based generation. Successful deployment relies on precise data mixture selection, alignment with specific inference engine constraints, and continuous monitoring of acceptance metrics. By effectively balancing draft model performance with target model verification, this approach enables substantial speedups and improved resource efficiency in production workloads, particularly for dense models operating under low-concurrency conditions.