NVIDIA GTC Studio with Insights from Schneider Electric

2026-03-30 · Source: NVIDIA · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure, Emerging Technologies & Innovation · Depth: Intermediate, long

Summary

Pankaj Sharma, Executive VP of Software and Services at Schneider Electric, discusses the critical power challenges and opportunities presented by the rapid proliferation of AI factories. He highlights that while AI factories demand significant energy, AI itself can optimize energy infrastructure and consumption. Schneider Electric focuses on "AI for energy" and "energy for AI," using AI algorithms to improve efficiency in data centers. Examples include a 10% energy saving in cooling systems through AI-driven HVAC optimization and reducing technical losses in digital grids by a few percentage points. The company emphasizes a holistic, system-level approach to energy management, integrating physical infrastructure design with software-defined controls and energy intelligence to accelerate sustainable AI scaling. This includes addressing existing "Brownfield" data centers by digitizing physical layers or, if necessary, replacing outdated infrastructure.

Key takeaway

For AI Architects and MLOps Engineers scaling AI infrastructure, recognize that energy efficiency is paramount and AI itself offers solutions. Focus on implementing "energy intelligence" through AI-driven software to optimize existing physical layers, like cooling and power distribution, and integrate digital twin methodologies for future AI factory designs. Your efforts should prioritize a holistic, system-level approach to avoid energy waste and ensure sustainable growth.

Key insights

AI can both consume and optimize energy, creating a circular relationship for sustainable scaling of AI infrastructure.

Principles

• System-level management is critical for optimal energy efficiency.
• Digital twins enhance AI factory design and energy optimization.

Method

Apply AI algorithms to historical performance data of energy systems (energy intelligence) to optimize physical infrastructure, such as HVAC and grid management, and inform future designs via digital twins.

In practice

• Use AI to optimize data center cooling systems for 10% energy savings.
• Implement energy intelligence software for grid loss reduction.
• Employ SimReady designs and digital twins for pre-construction optimization.

Topics

• AI Factories
• Energy Intelligence
• Data Center Cooling
• Digital Twins
• Grid Optimization

Best for: AI Architect, MLOps Engineer, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA.