AI Load Dynamics–A Power Electronics Perspective Yuzhuo Li, and Yunwei Li Department of Electrical and Computer Engineering University of Alberta, Edmonton, Canada

 

 

 

AI Load Dynamics–A Power Electronics Perspective Yuzhuo Li1, and Yunwei Li1 1Department of Electrical and Computer Engineering University of Alberta, Edmonton, Canada 

 Abstract—As AI-driven computing infrastructures rapidly scale, discussions around data center design often emphasize energy consumption, water and electricity usage, workload scheduling, and thermal management. However, these perspectives often overlook the critical interplay between AI-specific load transients and power electronics. This paper addresses that gap by examining how largescale AI workloads impose unique demands on power conversion chains and, in turn, how the power electronics themselves shape the dynamic behavior of AI-based infrastructure. We illustrate the fundamental constraints imposed by multi-stage power conversion architectures and highlight the key role of final-stage modules in defining realistic power slew rates for GPU clusters. Our analysis shows that traditional designs, optimized for slower-varying or CPU-centric workloads, may not adequately accommodate the rapid load ramps and drops characteristic of AI accelerators. To bridge this gap, we present insights into advanced converter topologies, hierarchical control methods, and energy buffering techniques that collectively enable robust and efficient power delivery. By emphasizing the bidirectional influence between AI workloads and power electronics, we hope this work can set a good starting point and offer practical design considerations to ensure future exascale-capable data centers can meet the stringent performance, reliability, and scalability requirements of nextgeneration AI deployments.

ORIGINAL LINK:  https://arxiv.org/pdf/2502.01647v2