Nvidia-backed trial shows AI data centers can flexibly adjust power use in near real time, with global implications for energy consumption  — suggests hyperscalers can reduce consumption as necessary, ensuring grid isn’t overloaded during peak demand

A U.K. study run by the National Grid, Nvidia, Emerald AI, and the Electric Power Research Institute (EPRI) just showed that AI data centers do not need to run at peak demand at all times. According to Bloomberg, the report suggests that hyperscalers can quickly adjust their power consumption as needed, ensuring that the grid is not overloaded when other consumers increase demand, while also using excess power from renewable energy when demand falls. If AI tech companies agree to this arrangement, it could allow them to go online much sooner, especially as the lack of available grid power has been hampering the deployment of more AI GPUs.

The system tested is very flexible and doesn’t require much advanced scheduling. Testing showed that it took less than a minute for a data center operator to reduce the amount of power it used to around 66%, and that one data center could even run at just 10% capacity for 10 hours.

This isn’t an ideal scenario for many hyperscalers, especially as they need as much power as they can get to maximize their expensive GPUs. However, insisting on this capability means that they’ll have to wait for the local grid to catch up (which can take years, if not decades), or they’ll have to deploy their own onsite generators (which can get expensive, unless they have deep pockets like OpenAI, and come with their own set of challenges).