Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
The rapid growth of generative artificial intelligence (AI) has introduced unprecedented computational demands, driving significant increases in the energy footprint of data centers. However, existing power consumption data is largely proprietary and reported at varying resolutions, creating challenges for estimating whole-facility energy use and planning infrastructure. In this work, we present a methodology that bridges this gap by linking high-resolution workload power measurements to whole-facility energy demand. Using NLR's high-performance computing data center equipped with NVIDIA H100 GPUs, we measure power consumption of AI workloads at 0.1-second resolution for AI training, fine...
| Authors | Roberto Vercellino, Jared Willard, Gustavo Campos et al. |
|---|---|
| Primary category | eess.SY |
| Published | 8 April 2026 |
| Last updated at source | 8 April 2026 |
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