Work

AI Surge Accelerates the Transition to Liquid Cooling: Rethinking Energy and Water Use in Data Center Cooling

Year: 2026

Type: preprint

Abstract: The power density of AI workloads is rapidly driving shifts to liquid cooling. This study rethinks and assesses the global impact on energy and water demand of this cooling transformation. Results suggest that liquid cooling can reduce cooling energy use by 25-56% relative to air cooling, offering substantial energy savings for GW-scale AI data centers. Best-practice PUE values could be as low as 1.07-1.08 for today’s systems and 1.03-1.04 for emerging designs and WUE values from 0.38 to 2.63 L kWh⁻¹, depending on temperature conditions. We identify global free-cooling rack inlet temperature thresholds of 33 °C for evaporative systems and 49 °C for dry systems, demonstrating the practicality of 100% free cooling in many locations. The model finds that minimum PUEs do not necessarily coincide with minimum total energy use. Results quantify the large sustainability potential of liquid cooling and provide actionable targets for data center design and policies.

Source: Research Square

DOI: 10.21203/rs.3.rs-10361694/v1

Authors: Shengwei Wang, Yingbo Zhang, Eric Masanet

Institutions: Hong Kong Polytechnic University, University of California, Santa Barbara

Language: English

Topic: Energy Efficiency in Computing

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

SDGs: Climate action, Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: green

OpenAlex ID: W7225423313

Cites: 19

Cited by:

Related to: 10