Software could ease AI data centres' power squeeze, researchers say
Researchers argue there is substantial room to cut data-centre energy use at the software and algorithm layers rather than only in hardware, Tom's Hardware reports. The IEA expects roughly 945TWh of electricity to serve AI demand by 2030 — about as much as Japan uses today — while the Uptime Institute's 2025 survey found average PUE has barely changed for six straight years. The University of Michigan's ML.Energy says running inference in FP8 on Alibaba's Qwen 3 235B A22B Thinking consumed about a third less energy than bfloat16, and its Perseus training optimiser cut training energy by up to 30% without reducing throughput or changing hardware; Nvidia says Blackwell power profiles can save up to 15% of energy while keeping 97% or more of performance.
With grids and chip supply both tight, software is seen as a cheaper, faster lever for controlling power use, though it cannot replace new chips or new electricity infrastructure.
Data center energy demand is shooting constantly upwards. By 2030, some 945TWh of electricity is expected to be used to meet AI’s demand; the International Energy Agency believes — that's as much electricity as Japan uses today. The energy crunch is inescapable, and the industry’s answer has largely been to focus on the physical: build more efficient GPUs and secure more power supply from an already tight grid. But is that myopic focus on hardware actually worth it? Instead, could most of the work running on those machines be cut back to save power?
Average power usage effectiveness (PUE) has barely changed for six successive years, according to the Uptime Institute’s 2025 survey. PUE can show whether cooling and power systems are wasteful, but doesn’t account for whether the software running on the servers is doing useful work. And given servers account for around 60% of electricity demand in a modern data center, while cooling ranges from about 7% in an efficient hyperscale site to more than 30% in a less-efficient enterprise facility, focusing on eking out more efficiency from the software side of the equation seems sensible.
#University of Michigan ML.Energy#Nvidia#Alibaba#Qwen 3 235B A22B Thinking#Perseus#International Energy Agency#Uptime Institute#ETH Zurich#SHI