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arXiv · 2602.09113

Benchmarking the Energy Savings with Speculative Decoding Strategies

Rohit Dutta·Paramita Koley·Soham Poddar·Janardan Misra·Sanjay Podder·Naveen Balani·Saptarshi Ghosh·Niloy Ganguly
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Abstract

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.

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Rohit Dutta, Paramita Koley, Soham Poddar, Janardan Misra, Sanjay Podder, Naveen Balani, Saptarshi Ghosh, Niloy Ganguly. 2026-02-09. Benchmarking the Energy Savings with Speculative Decoding Strategies. https://arxiv.org/abs/2602.09113

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