data-center-ai-workload-power

name

data-center-ai-workload-power

description

Data center AI workload power profiling and infrastructure planning. Methods for measuring generative AI workload power consumption at high resolution, scaling to whole-facility energy demand, and planning infrastructure for grid connection, microgrids, and on-site generation. Triggers: data center power, AI energy consumption, GPU power profiling, facility infrastructure planning, generative AI workload, H100 power measurement, MLCommons benchmark power.

Data Center AI Workload Power Profiling

Methods for measuring generative AI workload power consumption and scaling to whole-facility energy demand for infrastructure planning.

Overview

Paper: "Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning" (arXiv: 2604.07345v1, April 2026)

Key contribution: Bridges the gap between high-resolution workload power measurements and whole-facility energy demand estimation.

Workload Power Measurement

Benchmark-Based Profiling

Use standardized benchmarks for reproducible profiling:

• MLCommons benchmarks for model training and fine-tuning
• vLLM benchmarks for inference workloads

Measurement Setup

• NVIDIA H100 GPUs (representative hardware)
• 0.1-second resolution power sampling
• Capture temporal fluctuations during training/fine-tuning/inference

Power Profile Components

1. GPU power - Primary computation energy
2. Memory power - Data movement energy
3. Cooling overhead - Thermal management energy
4. Auxiliary systems - Network, storage, management

Whole-Facility Energy Modeling

Bottom-Up Event-Driven Model

Scale workload profiles to facility level:

1. Aggregate individual workload profiles
2. Model temporal distribution of user requests
3. Include infrastructure overhead (cooling, power distribution)
4. Capture realistic fluctuations

Energy Profile Characteristics

• Temporal fluctuations - Driven by AI workloads and user behavior
• Peak demand estimation - Maximum power draw periods
• Average demand - Baseline energy consumption
• Demand variability - Range of power fluctuations

Infrastructure Planning Applications

Grid Connection Planning

Use energy profiles to determine:

• Required power capacity from grid
• Peak demand management strategies
• Grid stability considerations

On-Site Energy Generation

Evaluate options:

• Solar/wind capacity sizing
• Battery storage requirements
• Backup generation sizing

Distributed Microgrids

Design resilient power infrastructure:

• Local generation capacity
• Load balancing strategies
• Failover mechanisms

Implementation Approach

Data Collection

# Power measurement workflow
1. Configure H100 GPU measurement setup
2. Run MLCommons/vLLM benchmark workloads
3. Record power at 0.1s resolution
4. Capture full workload duration
5. Export power profile data

Scaling Methodology

Workload Power → Facility Energy:
1. Sum individual workload profiles
2. Apply temporal user-behavior model
3. Add infrastructure overhead factors
4. Generate facility-level time series

Key Metrics

Metric	Description	Application
Peak Power (W)	Maximum GPU power draw	Grid capacity planning
Average Power (W)	Mean power consumption	Energy cost estimation
Energy (J)	Total energy per workload	Operating cost analysis
Power Variance	Power fluctuation range	Infrastructure stability

Practical Applications

• New data center design - Size infrastructure for AI workloads
• Capacity expansion - Plan for additional GPU deployments
• Cost estimation - Predict energy costs for AI operations
• Sustainability - Evaluate renewable energy integration

Reference

• Paper: arXiv:2604.07345v1
• PDF: https://arxiv.org/pdf/2604.07345v1
• Category: eess.SY (Systems and Control)