What question did this study set out to answer?

To analyze energy efficiency in hyperscale data centers and its relation to growing demands from AI workloads.

April 6, 2026Open Access

Energy Efficiency Optimization In Hyperscale Data Centers

Key Points

To analyze energy efficiency in hyperscale data centers and its relation to growing demands from AI workloads.
Assessment of energy efficiency trends in hyperscale infrastructure
Analysis of the impact of GPU/accelerator-based AI on energy and cooling demands
Review of recent efficiency gains over the past decade
Efficiency gains have helped decouple compute demand growth from electricity use
GPU/accelerator-based AI is increasing pressure on energy and cooling capacity needs
Hyperscale data centers are critical in the global energy system

Abstract

Hyperscale data centers—very large, industrialized facilities that deliver cloud-scale compute, storage, and networking—have become a central node in the global energy system because they concentrate electrical load, water demand, and waste heat while enabling rapidly growing digital services and AI workloads. Over the past decade, efficiency gains in hyperscale infrastructure and IT operations have helped decouple growth in compute demand from the growth in electricity use, but the acceleration of GPU/accelerator-based AI is now reintroducing strong upward pressure on both energy and cooling capacity requirements (Masanet et al., 2020; International Energy Agency IEA, 2025; U.S. Department of Energy DOE, 2024).1

Energy Efficiency Optimization In Hyperscale Data Centers

Key Points

Abstract

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