Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning
2026-04-08 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster ComputingMachine Learning
AI summaryⓘ
The authors studied how much power AI workloads use in a big computer center by measuring power use very frequently while running different AI tasks like training and inference. They used standardized tests to make sure their measurements can be compared and repeated by others. Then, they combined these detailed power measurements to estimate the energy use of the entire facility. This helps understand how AI affects overall energy needs and can guide planning for power supply and infrastructure in data centers.
generative artificial intelligencepower consumptiondata centersAI traininginferenceMLCommons benchmarksenergy modelingNVIDIA H100 GPUsinfrastructure planninghigh-performance computing
Authors
Roberto Vercellino, Jared Willard, Gustavo Campos, Weslley da Silva Pereira, Olivia Hull, Matthew Selensky, Juliane Mueller
Abstract
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-tuning and inference jobs. Workloads are characterized using MLCommons benchmarks for model training and fine-tuning, and vLLM benchmarks for inference, enabling reproducible and standardized workload profiling. The dataset of power consumption profiles is made publicly available. These power profiles are then scaled to the whole-facility-level using a bottom-up, event-driven, data center energy model. The resulting whole-facility energy profiles capture realistic temporal fluctuations driven by AI workloads and user-behavior, and can be used to inform infrastructure planning for grid connection, on-site energy generation, and distributed microgrids.