Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding

TL;DR

This paper provides a comprehensive survey of Speculative Decoding, a novel approach that accelerates Large Language Model inference by drafting and verifying multiple tokens simultaneously, reducing latency.

Contribution

It offers a formal definition, analyzes key strategies, and compares leading methods, serving as a foundational overview for future research in speculative decoding.

Findings

Speculative Decoding significantly reduces inference latency.

Key strategies include drafter selection and verification methods.

Comparative analysis highlights the most effective approaches.

Abstract

To mitigate the high inference latency stemming from autoregressive decoding in Large Language Models (LLMs), Speculative Decoding has emerged as a novel decoding paradigm for LLM inference. In each decoding step, this method first drafts several future tokens efficiently and then verifies them in parallel. Unlike autoregressive decoding, Speculative Decoding facilitates the simultaneous decoding of multiple tokens per step, thereby accelerating inference. This paper presents a comprehensive overview and analysis of this promising decoding paradigm. We begin by providing a formal definition and formulation of Speculative Decoding. Then, we organize in-depth discussions on its key facets, such as drafter selection and verification strategies. Furthermore, we present a comparative analysis of leading methods under third-party testing environments. We aim for this work to serve as a catalyst for further research on Speculative Decoding, ultimately contributing to more efficient LLM inference.