As AI infrastructure expands, data centers are placing enormous new demands on electrical grids. And much of the conversation focuses on how to generate and deliver enough power to keep up.

But there’s another side of the equation:

What if AI required less electricity to do the work in the first place?

Our latest podcast explores how data movement contributes to AI’s power demands—and why GSI Technology’s Gemini-II APU takes a fundamentally different approach.

By bringing computation closer to where data resides, compute-in-memory can reduce unnecessary data movement, opening the door to greater efficiency for workloads that fit the architecture.

Because every watt you don’t consume is a watt you don’t have to generate, transmit, cool, or pay for.

Hear why performance per watt could become one of the defining metrics of the AI era:

[Downloadable Transcript]

*AI-crafted. Human-perfected.