How to manage your power bill while adopting AI

Embracing AI doesn't mean blowing up your electric bill. Here's how to minimize the pain.

virtual binary data center servers and cables
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Artificial intelligence (AI) and machine learning (ML) can be invaluable tools to spur innovation, but they have different management requirements than typical enterprise IT applications that run at moderate CPU and memory utilization rates. Because AI and ML tend to run intense calculations at very high utilization rates, power and cooling costs can consume a higher proportion of the budget than an IT group might expect.

It's not a new problem, but the impact is intensifying.

As more CPU-heavy applications such as data warehousing and business intelligence became prevalent, IT was often oblivious to the electric bill it was racking up – particularly since the bill usually goes to the ops department, not IT.

"The data-science team leaders often have carte blanche to just process anything, anytime," says Mark Swartz, CEO and founder of AI developer Neural. "The days of these luxurious approaches to solving heavy compute requirements will start to trend down in the next five years."

One reason for greater scrutiny of power and cooling costs is that AI often relies on high-performance computing (HPC), while data warehousing and business intelligence applications can be run on standard systems. HPC and AI run much hotter, and no one should be blindsided by the increased bill, says Addison Snell, CEO of Intersect360, a research firm specializing in HPC issues.

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