Speculative Decoding

Speculative Decoding accelerates LLM inference by using a small "draft" model to propose several candidate tokens at once, which the larger "target" model then verifies in a single forward pass. When the target accepts a draft prediction, it saves the cost of a full forward pass for that token — yielding 2-3x speedups on common workloads. The technique was popularized by Google's 2023 paper on speculative sampling and has been widely adopted in production inference engines including vLLM, TensorRT-LLM, and the OpenAI and Anthropic backend systems. Variants like Medusa (multiple decoding heads on the same model), EAGLE (an enhanced draft architecture), and self-speculative decoding push throughput even further. Combined with continuous batching and paged attention, speculative decoding helps modern LLM serving achieve dramatically better economics than naive autoregressive generation. AI governance, AI compliance, and AI risk management programs document inference techniques in deployment records as part of responsible AI reproducibility evidence for enterprise AI.

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