How AI Load Swings Reduce Datacenter Power Conversion Efficiency

AI training and inference workloads make GPU power swing repeatedly between compute and communication phases. As a result, the cascaded converters in a datacenter rack keep moving between operating points with different efficiencies. This paper uses an energy based, cycle averaged method to quantify that effect along a 240 V AC to 400 V DC to 51 V DC rack power path, using measured efficiency curves of commercially available GaN based PFC and LLC converters. For a 1 Hz, 50% duty-cycle load swin…

Source: arXiv cs.AR (Hardware Architecture), published 2026-10-27. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern