Benchmarking the Energy Savings with Speculative Decoding Strategies

2026-02-09

AI Summary

This study addresses the lack of systematic evaluation of energy consumption characteristics in speculative decoding strategies for large language models. It presents the first comprehensive quantification of fine-grained energy usage across diverse speculative decoding methods, examining their performance under varying model scales and architectures, decoding strategies, and datasets. The work uncovers the synergistic effects of model design, algorithmic choices, and data properties on energy efficiency, identifying key factors that determine the energy efficacy of speculative decoding. These findings provide empirical grounding and actionable insights for optimizing large-model inference toward lower energy consumption.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Search and Retrieval-Augmented AI: Large language models for search
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

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.

Problem

• speculative decoding
• energy consumption
• large language models
• inference cost
• energy efficiency

Innovation

• speculative decoding
• energy efficiency
• large language models
• inference optimization
• benchmarking