New Nvidia+Oracle and others paper says data centers can cut power fast and still protect important jobs during grid stress.

i.e. data center power can be scheduled much like computing work.

Changes the way AI data centers can be viewed by power grids.

Instead of treating them as giant machines that always need full power, it shows they can lower, delay, or move some computing work when the grid is under stress.

The problem is that grid planners often treat large AI data centers as fixed loads that always need full power.

This work shows that many AI tasks are more flexible than that because training, batch inference, and lower-priority jobs can slow down, wait, or move.

The system connects grid signals to AI job scheduling, GPU power limits, and live power measurements, so the cluster can follow requested power targets.

In a real 130 kW GPU cluster, it met 200+ power targets and reduced power by about 30% within 40 seconds.

It also held lower power for hours, followed carbon signals, and shifted inference traffic from Virginia to Illinois when one region was constrained.

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– arxiv. org/abs/2606.25098

Title: "Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute"