ai-workload-power-profiling

name	ai-workload-power-profiling
description	AI workload power profiling for data center infrastructure planning. Measure, model, and scale power consumption of AI training, fine-tuning, and inference jobs. Use when: data center energy planning, GPU power optimization, AI infrastructure design, generative AI workload analysis, MLCommons benchmarks, vLLM inference profiling, H100 GPU power measurement.

AI Workload Power Profiling

Overview

Bridge high-resolution workload power measurements to whole-facility data center energy demand for generative AI infrastructure planning.

Core Concepts

1. Power Measurement Methodology

Component	Resolution	Purpose
GPU Power	0.1 second	Real-time workload profiling
Facility Power	Event-driven	Infrastructure planning
User Behavior	Stochastic	Temporal fluctuation modeling

2. Standardized Benchmarks

Training/Fine-tuning:

• MLCommons benchmarks for reproducible profiling
• H100 GPU power consumption patterns
• Training vs. fine-tuning power differences

Inference:

• vLLM benchmarks for inference profiling
• Batch size vs. power trade-offs
• Latency-power optimization

3. Bottom-up Energy Modeling

Workload Power (0.1s resolution)
    ↓ Scale
GPU Cluster Power
    ↓ Aggregate
Facility-level Energy Demand
    ↓ Model
Grid Connection Planning

Implementation

Power Profiling Workflow

# Key steps for AI workload power profiling

1. Define workload (MLCommons/vLLM benchmark)
2. Measure GPU power at 0.1s resolution
3. Characterize temporal patterns (training vs inference)
4. Scale to facility-level using event-driven model
5. Plan infrastructure (grid, microgrid, on-site generation)

Critical Metrics

Metric	Unit	Significance
Peak Power	kW	Grid connection sizing
Average Power	kW	Energy cost estimation
Power Variance	kW²	Storage requirement
Duration	hours	Total energy consumption

Design Implications

Infrastructure Planning

1. Grid Connection

   • Peak power determines minimum capacity
   • Temporal fluctuations impact grid stability

2. On-site Generation

   • Average power for solar sizing
   • Peak power for storage sizing

3. Distributed Microgrids

   • Workload distribution for load balancing
   • Geographic diversity for resilience

Use Cases

Scenario	Application
New Data Center	Grid connection capacity planning
Existing Facility	Power optimization analysis
AI Cluster Expansion	Infrastructure scaling
Inference Service	Latency-power trade-off analysis
Training Pipeline	Energy cost estimation

Best Practices

1. Resolution Matters

   • 0.1s captures transient spikes
   • Lower resolution misses peak events

2. Standardized Workloads

   • MLCommons/vLLM for reproducibility
   • Enable cross-facility comparison

3. User Behavior Integration

   • Temporal patterns from real usage
   • Stochastic arrival modeling

4. Public Dataset Availability

   • Power profiles published openly
   • Enable community benchmarking

Key Takeaways

• AI workloads create unprecedented power demands
• High-resolution profiling essential for planning
• Bottom-up modeling bridges GPU-to-facility gap
• Standardized benchmarks enable reproducibility

Reference

Paper: "Measurement of Generative AI Workload Power Profiles for Whole-Facility Data Center Infrastructure Planning" arXiv: 2604.07345v1 Authors: Roberto Vercellino, Jared Willard, et al. Date: 2026-04-08