AI-driven energy demand is forcing a step change in datacentre design, centered on powering and cooling GPUs at levels beyond traditional air-cooled facilities. GPU thermal and electrical densities make fan-based air cooling insufficient for large language model training and inferencing. This creates an inflection point that requires direct-to-chip liquid cooling and changes rack-level power approaches. The shift supports the move toward AI factories rather than conventional datacentres, because the cost and complexity of meeting these requirements can exceed what enterprises can manage on-premise. Construction momentum at a large AI site and the broader transformation of the datacentre landscape are tied to these hardware-driven constraints, including impacts on power grids and water use.
"The enormous increase in energy consumption driven by AI has brought a step change in datacentre design. Core to this is the requirement to power and cool GPUs to an extent that was not necessary in "traditional" air-cooled datacentres. Hence the advent of the AI factory."
"Datacentre cooling has been a predictable exercise in industrial heating, ventilation and air-conditioning (HVAC) design, where one slotted servers into racks and blew chilled air across the chassis. AI has rewritten the story."
"The hardware that powers the AI revolution - GPU especially - operates at thermal and electrical densities that render traditional air-cooling methods obsolete. The silicon demands of large language model training and inferencing cannot be sustained by more or faster fans."
"Instead, the industry faces an inflection point that mandates direct-to-chip liquid cooling and a transformation of rack-level powe"
Read at ComputerWeekly.com
Unable to calculate read time
Collection
[
|
...
]