Related Datasets

Dataset of Generative AI Workload Power Profiles

Publicly accessible License

This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model. This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems.The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison.The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment.

Citation Formats

TY  - DATA
AB  - This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model. This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems.The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison.The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment.
AU  - Vercellino
A2  - Willard
A3  - Campos
A4  - da Silva Pereira
A5  - Hull
A6  - Selensky
A7  - Mueller
DB  - Open Energy Data Initiative (OEDI)
DP  - Open EI | National Laboratory of the Rockies
DO  - 
KW  - AI workloads
KW  - Data Centers
KW  - Generative AI
KW  - Power Measurements
KW  - GPU power
KW  - high performance computing
LA  - English
DA  - 2026/03/31
PY  - 2026
PB  - National Laboratory of the Rockies
T1  - Dataset of Generative AI Workload Power Profiles
UR  - https://data.openei.org/submissions/8651
ER  -

Export Citation to RIS

Vercellino, et al. Dataset of Generative AI Workload Power Profiles. National Laboratory of the Rockies, 31 March, 2026, NREL. https://data.nlr.gov/submissions/312.

Vercellino, Willard, Campos, da Silva Pereira, Hull, Selensky, & Mueller. (2026). Dataset of Generative AI Workload Power Profiles. [Data set]. NREL. National Laboratory of the Rockies. https://data.nlr.gov/submissions/312

Vercellino, Willard, Campos, da Silva Pereira, Hull, Selensky, and Mueller. Dataset of Generative AI Workload Power Profiles. National Laboratory of the Rockies, March, 31, 2026.  Distributed by NREL. https://data.nlr.gov/submissions/312

@misc{OEDI_Dataset_8651,
title = {Dataset of Generative AI Workload Power Profiles},
author = {Vercellino and Willard and Campos and da Silva Pereira and Hull and Selensky and Mueller},
abstractNote = {This dataset provides a collection of high-resolution (5/10 Hz or every 0.2/0.1 seconds) power consumption profiles for generative artificial intelligence (GenAI) workloads executed on NLR's High Performance Computing (HPC) platform Kestrel. The dataset also includes examples of representative whole-facility power profiles generated using a bottom-up, event-driven, data center energy model. This dataset is designed to support research in energy modeling, infrastructure planning, energy system integration, and sustainability analysis for AI-driven computing systems.The dataset captures time-resolved electrical power measurements across a diverse set of configurations, including variations in job type (inference vs. training), workload (LLM vs. image generation), datasets, and number of compute nodes. Power traces are provided in a standardized format and include both raw/instantaneous and aggregated files. Each profile is accompanied by metadata describing workload parameters, enabling reproducibility and cross-study comparison.The dataset is intended for use in applications such as data center infrastructure planning, energy modeling, demand response and grid impact studies, and development and validation of system-level simulation tools. By making these workload-specific power profiles publicly available, this dataset aims to address the current lack of open, empirical energy data for generative AI systems and to facilitate transparent, reproducible research on the energy and environmental impacts of large-scale AI deployment.},
url = {https://data.nlr.gov/submissions/312},
year = {2026},
howpublished = {NREL, National Laboratory of the Rockies, https://data.nlr.gov/submissions/312},
note = {Accessed: 2026-04-03}
}

Details

Data from Mar 31, 2026

Last updated Mar 31, 2026

Submitted Mar 31, 2026