





















Authors:Ehsan K. Ardestani, Leonardo Piga, Jovan Stojkovic, Pavan Balaji, Mustafa Ozdal, Mikel Jimenez Fernandez, Mihaela Dimovska, Luka Tadic, Hao Shen, Devika Vishwanath, Richa Mishra, Melaku Mihret, Valentin Andrei, Mauricio Cespedes, Julien Prigent, James Monahan, Tyler Graf, Bin Li, Charles Marquez, Shobhit Kanaujia, Kaushik Veeraraghavan, Chunqiang Tang
Abstract:The electric power supply for AI data centers is now the most significant bottleneck in the race toward Artificial General Intelligence, surpassing even the constraint of AI accelerator availability. To our knowledge, this paper is the first to describe the end-to-end power management process for a hyper-scale AI datacenter; from early power planning to accommodate next-generation accelerators 6--12 months before their general availability, to tuning power settings after large scale deployment, and finally to dynamic, runtime power management for evolving workloads. We present detailed power measurements for a 150 MW datacenter hosting a cluster of 83K GB200 GPUs. We share insights from building this state-of-the-art AI cluster. We hope this work encourages practitioners across the industry to share their own experiences as well.
| Subjects: | Hardware Architecture (cs.AR); Distributed, Parallel, and Cluster Computing (cs.DC); Systems and Control (eess.SY) |
| Cite as: | arXiv:2605.24461 [cs.AR] |
| (or arXiv:2605.24461v1 [cs.AR] for this version) | |
| https://doi.org/10.48550/arXiv.2605.24461 arXiv-issued DOI via DataCite (pending registration) |
From: Ehsan K. Ardestani [view email]
[v1]
Sat, 23 May 2026 08:18:01 UTC (8,529 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。