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研究:软件优化可缓解AI数据中心电力压力

AI 总结

Tom's Hardware 报道,研究人员认为在硬件之外,软件与算法层面仍有大量省电空间。国际能源署预计到 2030 年 AI 相关数据中心用电将达约 945TWh,相当于日本当前全国用电量;而 Uptime Institute 2025 年调查显示,数据中心平均 PUE 已连续六年几乎没有改善。密歇根大学 ML.Energy 团队的测试称,Alibaba Qwen 3 235B A22B Thinking 以 FP8 精度推理比 bfloat16 省电约三分之一,其 Perseus 训练优化器在不降低吞吐、不改动硬件的情况下最多减少 30% 训练能耗;Nvidia 则表示 Blackwell 的功耗配置可在保留 97% 以上性能的同时省电最多 15%。

深度见解

在电网与芯片供给都吃紧的情况下,软件层被视为一种成本更低、见效更快的控电手段,但它无法替代新芯片和新电力设施。

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

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