# Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding

arXiv: https://arxiv.org/abs/2411.13157 · HTML: https://mlsanity.com/paper/2411.13157/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving.
- Builds on 28 of its 34 references that are in the map.
- First posted on arXiv 20 Nov 2024.

## Placed in

- Efficient LLM inference and serving / [Speculative decoding](https://mlsanity.com/inference/speculative-decoding/)

## Builds on (most cited first, 28 of 28)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Curious Case of Neural Text Degeneration](https://mlsanity.com/paper/1904.09751/) (2019)
- [Fast Inference from Transformers via Speculative Decoding](https://mlsanity.com/paper/2211.17192/) (2022)
- [Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads](https://mlsanity.com/paper/2401.10774/) (2024)
- [Fast Transformer Decoding: One Write-Head is All You Need](https://mlsanity.com/paper/1911.02150/) (2019)
- [EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty](https://mlsanity.com/paper/2401.15077/) (2024)
- [EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees](https://mlsanity.com/paper/2406.16858/) (2024)
- [Blockwise Parallel Decoding for Deep Autoregressive Models](https://mlsanity.com/paper/1811.03115/) (2018)
- [LayerSkip: Enabling Early Exit Inference and Self-Speculative Decoding](https://mlsanity.com/paper/2404.16710/) (2024)
- [Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding](https://mlsanity.com/paper/2401.07851/) (2024)
- [QServe: W4A8KV4 Quantization and System Co-design for Efficient LLM Serving](https://mlsanity.com/paper/2405.04532/) (2024)
- [Hydra: Sequentially-Dependent Draft Heads for Medusa Decoding](https://mlsanity.com/paper/2402.05109/) (2024)
- [A Thorough Examination of Decoding Methods in the Era of LLMs](https://mlsanity.com/paper/2402.06925/) (2024)
- [MagicDec: Breaking the Latency-Throughput Tradeoff for Long Context Generation with Speculative Decoding](https://mlsanity.com/paper/2408.11049/) (2024)
- [SpecDec++: Boosting Speculative Decoding via Adaptive Candidate Lengths](https://mlsanity.com/paper/2405.19715/) (2024)
- [Learning Harmonized Representations for Speculative Sampling](https://mlsanity.com/paper/2408.15766/) (2024)
- [OPT-Tree: Speculative Decoding with Adaptive Draft Tree Structure](https://mlsanity.com/paper/2406.17276/) (2024)
- [SpecExec: Massively Parallel Speculative Decoding for Interactive LLM Inference on Consumer Devices](https://mlsanity.com/paper/2406.02532/) (2024)
- [Lookahead: An Inference Acceleration Framework for Large Language Model with Lossless Generation Accuracy](https://mlsanity.com/paper/2312.12728/) (2023)
- [TurboSpec: Closed-loop Speculation Control System for Optimizing LLM Serving Goodput](https://mlsanity.com/paper/2406.14066/) (2024)
- [Recursive Speculative Decoding: Accelerating LLM Inference via Sampling Without Replacement](https://mlsanity.com/paper/2402.14160/) (2024)
- [Direct Alignment of Draft Model for Speculative Decoding with Chat-Fine-Tuned LLMs](https://mlsanity.com/paper/2403.00858/) (2024)
- [Hardware-Aware Parallel Prompt Decoding for Memory-Efficient Acceleration of LLM Inference](https://mlsanity.com/paper/2405.18628/) (2024)
- [ProPD: Dynamic Token Tree Pruning and Generation for LLM Parallel Decoding](https://mlsanity.com/paper/2402.13485/) (2024)
- [BASS: Batched Attention-optimized Speculative Sampling](https://mlsanity.com/paper/2404.15778/) (2024)
- [Aladdin: Joint Placement and Scaling for SLO-Aware LLM Serving](https://mlsanity.com/paper/2405.06856/) (2024)
- [Chimera: A Lossless Decoding Method for Accelerating Large Language Models Inference by Fusing all Tokens](https://mlsanity.com/paper/2402.15758/) (2024)
- [Exploring and Improving Drafts in Blockwise Parallel Decoding](https://mlsanity.com/paper/2404.09221/) (2024)

## Built on by (most cited first, 7 of 7)

- [A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond](https://mlsanity.com/paper/2503.21614/) (2025)
- [A Survey of Context Engineering for Large Language Models](https://mlsanity.com/paper/2507.13334/) (2025)
- [TETRIS: Optimal Draft Token Selection for Batch Speculative Decoding](https://mlsanity.com/paper/2502.15197/) (2025)
- [DReSD: Dense Retrieval for Speculative Decoding](https://mlsanity.com/paper/2502.15572/) (2025)
- [Small Language Models (SLMs) Can Still Pack a Punch: A survey (updated 2026)](https://mlsanity.com/paper/2501.05465/) (2025)
- [MineDraft: A Framework for Batch Parallel Speculative Decoding](https://mlsanity.com/paper/2603.18016/) (2026)
- [SENSE: Semantic Embedding Navigation with Soft-gated Evaluation for Retrieval-based Speculative Decoding](https://mlsanity.com/paper/2606.00021/) (2026)

## Abstract (arXiv)

Efficient inference in large language models (LLMs) has become a critical focus as their scale and complexity grow. Traditional autoregressive decoding, while effective, suffers from computational inefficiencies due to its sequential token generation process. Speculative decoding addresses this bottleneck by introducing a two-stage framework: drafting and verification. A smaller, efficient model generates a preliminary draft, which is then refined by a larger, more sophisticated model. This paper provides a comprehensive survey of speculative decoding methods, categorizing them into draft-centric and model-centric approaches. We discuss key ideas associated with each method, highlighting their potential for scaling LLM inference. This survey aims to guide future research in optimizing speculative decoding and its integration into real-world LLM applications.