Where is the world’s largest refrigerator? Well, it is not in some mega grocery barn or parked at an ice-cream fairyland. It is always whirring busily at some huge nuclear fusion lab or juicing up a quantum lab or firing up the cylinders of a supercomputer.
So it was not a surprise when someone first questioned – Hey, how much power does AI slurp- if it has to run all those large models and churn up that many answers, and that fast? The question is not boring. As to whether it is too soon or too serious – now that, calls for some asking around.
AI-NOT AN ERMINE FOR SURE
Let’s start with Indranil Bandyopadhyay, principal analyst at Forrester (who packs special domain expertise in areas like data science and AI). “AI’s power consumption angle is a valid question to ask- especially after the heightened interest seen with Gen AI. But we cannot single it out - it is contributing to the carbon footprint that has been created by other industries already. That said - large models – especially in computer vision areas and the rise of multi-modality can add more power burden on today’s models.”
Uptime Intelligence estimates that generative AI annualized energy accounted for around 2.3 percent of the total grid power consumption by data centers in the first quarter of 2024. And this could reach 7.3 percent by the end of 2025.
When fully operational, an AI training system demands a large amount of power, up to two megawatts (MW), Jay Dietrich, Research Director of Sustainability at Uptime Institute gives a backof-the-envelope glimpse. “In the context of a 300 to 500 MW cloud data center complex run by large hyperscalers, this is a small portion of the installed power capacity. As more and larger training systems are deployed, they will more quickly grow the power demand at a facility in a smaller footprint compared to traditional compute.”
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