πŸ“„
Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding paper by Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
⭐ 59.3
πŸ“„
Paper

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 2401.07851
Free2AITools Nexus Index
59.3
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 89
P: Popularity 67
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

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 ...

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Semantic Scholar 217 Citations
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Registry ID 2401.07851
License ArXiv
Provider semantic_scholar
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Cite this paper

Academic & Research Attribution

BibTeX 
@misc{arxiv_2401_07851,
  author = {Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui},
  title = {Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding Paper},
  year = {2026},
  howpublished = {\url{https://arxiv.org/abs/2401.07851}},
  note = {Accessed via Free2AITools.}
}
APA Style
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui. (2026). Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding [Paper]. Free2AITools. https://arxiv.org/abs/2401.07851

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βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 89
Popularity (P) 67
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding: Authority (A:89), Popularity (P:67), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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πŸ“ Executive Summary

"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 ..."

❝ Cite Node 

@article{Xia2026Unlocking,
  title={Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding},
  author={Heming Xia and Zhe Yang and Qingxiu Dong and Peiyi Wang and Yongqi Li and Tao Ge and Tianyu Liu and Wenjie Li and Zhifang Sui},
  journal={arXiv preprint arXiv:2401.07851},
  year={2026}
}

πŸ‘₯ Collaborating Minds

Heming Xia Zhe Yang Qingxiu Dong Peiyi Wang Yongqi Li Tao Ge Tianyu Liu Wenjie Li Zhifang Sui

πŸ”— Full Paper

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πŸ“Š Research Signals

πŸ“ˆ217CitationsSemantic Scholar
πŸ›οΈ89AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
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πŸ†” Identity & Source

id
2401.07851
slug
2401.07851
source
semantic_scholar
author
Heming Xia, Zhe Yang, Qingxiu Dong, Peiyi Wang, Yongqi Li, Tao Ge, Tianyu Liu, Wenjie Li, Zhifang Sui
license
ArXiv
tags
paper, research, academic

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

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citations
217

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