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2026-09-15 · Source：Nvidia Blog
Silicon Valley Power signaled an AI factory to adjust consumption on an August evening as Varun Sivaram's Emerald AI team and about forty others watched on Zoom, NVIDIA recounts.
NVIDIA has published an account of a grid-coordination episode in which a utility called on a Silicon Valley AI factory to adjust its power consumption in real time. The scene, framed on the company's blog as the business of turning megawatts into tokens, unfolded on a sweltering August evening, as the sun dropped and air-conditioning loads spiked across the region.
At that point, Silicon Valley Power sent a signal to the AI factory instructing it to modify its power draw. Watching remotely was Varun Sivaram, connected via Zoom with about forty others. His team at Emerald AI followed from their San Francisco conference room, with engineers also watching on.
The episode anchors NVIDIA's argument that AI data centers should be run as factories, with electricity as the raw input and tokens as the finished output. Framing compute in production terms puts power management — when a facility draws electricity, and how quickly that draw can flex — on equal footing with throughput. A utility signal arriving during a peak-load evening, and an AI factory positioned to receive it, shows what that coordination looks like in practice. The request landed on an evening when air-conditioning demand was climbing — precisely the conditions under which utilities most need large consumers to flex.
For buyers and operators of AI compute, the significance lies in the widening interface between utilities and large-scale facilities. Where grids run tight, the ability to modulate megawatt consumption on request becomes part of how an AI factory maximizes production, in NVIDIA's telling. Teams like Emerald AI's, watching consumption signals land in real time, suggest demand-response coordination is moving from concept to operating procedure for AI infrastructure.
Mots-clés: NVIDIA · Emerald AI · Silicon Valley Power · Varun Sivaram · Data Center · Demand Response
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