Schneider Electric envisions AI datacenters of the future

The infrastructure behind popular AI workloads is so demanding that Schneider Electric has suggested it may be time to reevaluate the way we build datacenters.], the French multinational broke down several of the factors that make accommodating AI workloads so challenging and offered its guidance for how future datacenters could be optimized for them. The bad news is some of the recommendations may not make sense for existing facilities.

According to Schneider, this is already at odds with what most datacenters can manage at 10-20kW per rack. This problem is exacerbated by the fact that training workloads benefit heavily from maximizing the number of systems per rack as it reduces network latency and costs associated with optics. The situation isn't nearly as dire for inferencing – the act of putting trained models to work generating text, images, or analyzing mountains of unstructured data – as fewer AI accelerators per task are required compared to training.

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