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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.
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