mlsanityData to 7 Oct 2026

Benchmarking the Energy Savings with Speculative Decoding Strategies

arXiv 2602.09113 ↗First posted 9 Feb 2026cs.LG cs.AI cs.CLcited by 0

On the map: Efficient LLM inference and serving → Speculative decoding

Abstract (arXiv)

Speculative decoding has emerged as an effective method to reduce latency and inference cost of LLM inferences. However, there has been inadequate attention towards the energy requirements of these models. To address this gap, this paper presents a comprehensive survey of energy requirements of speculative decoding strategies, with detailed analysis on how various factors -- model size and family, speculative decoding strategies, and dataset characteristics -- influence the energy optimizations.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Placed in Speculative decoding, one of 12 directions in Efficient LLM inference and serving. Builds on 11 of its 19 references that are in the map. First posted on arXiv 9 Feb 2026.

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11 of 19 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025EAGLE-3: Scaling up Inference Acceleration of Large Language Models via Training-Time Test3 Mar
  2. 2024EAGLE-2: Faster Inference of Language Models with Dynamic Draft Trees24 Jun
  3. 2024EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty26 Jan
  4. 2024Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads19 Jan
  5. 2023Accelerating Large Language Model Decoding with Speculative Sampling2 Feb
  6. 2022Fast Inference from Transformers via Speculative Decoding30 Nov

Other references · 5

  1. 2021Evaluating Large Language Models Trained on Codecited by 8,053
  2. 2019HuggingFace's Transformers: State-of-the-art Natural Language Processingcited by 2,201
  3. 2016Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyondcited by 809
  4. 2022The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrinkcited by 183
  5. 2023How to estimate carbon footprint when training deep learning models? A guide and reviewcited by 34

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Similar, not cited

Close in wording, no citation either way

  1. 2023Accelerating LLM Inference with Staged Speculative Decoding
  2. 2025Tutorial Proposal: Speculative Decoding for Efficient LLM Inference
  3. 2024Closer Look at Efficient Inference Methods: A Survey of Speculative Decoding
  4. 2025Scaling LLM Speculative Decoding: Non-Autoregressive Forecasting in Large-Batch Scenarios
  5. 2024A Theoretical Perspective for Speculative Decoding Algorithm
  6. 2025Speculative Decoding: Performance or Illusion?
  7. 2024Unlocking Efficiency in Large Language Model Inference: A Comprehensive Survey of Speculative Decoding
  8. 2026SPEED-Bench: A Unified and Diverse Benchmark for Speculative Decoding