Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
By Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui. 2024
297 citations recorded by the source corpus.
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.
Subfields: Large Language Models, Efficiency & Compression
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In Large Language Models
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Citations in the corpus
- EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees (2024, background): “Our comparative experiments utilized Spec-Bench (Xia et al., 2024).”
- Break the Sequential Dependency of LLM Inference Using Lookahead Decoding (2024, background): “It shows notable speedup without a draft model and can linearly decrease the decoding steps with exponential investment in per-step FLOPs.”