How Schneider Electric and NVIDIA are redefining AI data center design

Traditional data center infrastructure, built for an earlier era of computing, faces new challenges in meeting today's demands.  Foundational AI models - neural networks trained on vast datasets to power everything from medical diagnostics to personalized education - now demand energy and compute resources on a scale once reserved for entire data centers - measured in megawatts rather than kilowatts How do we build the physical backbone for AI workloads that demand unprecedented density, power, and cooling capacity?

This challenge is why we've partnered with NVIDIA to develop validated AI reference designs - comprehensive blueprints that enable data center operators to deploy high-density AI clusters efficiently, reliably, and sustainably. These are engineered solutions addressing real constraints, helping organizations navigate the transition from legacy infrastructure to AI-ready facilities.

The AI Infrastructure challenge

AI workloads are fundamentally different from traditional enterprise computing. NVIDIA's latest GPU architectures - the GB200 and GB300 NVL72-based clusters - can draw up to 142 kilowatts per rack. To put that in perspective, traditional data center racks typically consume 5-15 kilowatts. A single AI cluster can demand 7.5 megawatts of power, generating thermal loads that legacy cooling systems have no hope of managing.

In one of our earlier reports, "The AI Disruption," we identified these physical constraints as the primary barrier to AI adoption at scale. Power distribution systems designed for moderate loads buckle under extreme densities. Air cooling becomes impractical. Standard racks lack the structural integrity for liquid-cooled equipment. Even the electrical characteristics change. Short-circuit currents, breaker coordination, and arc flash risks all require new approaches.

The data center industry needs more than incremental improvements. It needs new design paradigms that combine efficiency, resilience, and sustainability from the ground up. Without them, the AI revolution risks being infrastructure-constrained.

Designing for the AI future

AI reference designs provide the solution. Think of them as validated blueprints - system-level specifications that define performance requirements, physical layouts, component lists, and energy management strategies. Working with NVIDIA, we've jointly engineered six distinct design scenarios, each addressing specific deployment needs:

Retrofit designs for existing facilities:

• Air-cooled configurations supporting up to 40 kW per rack in a 1 MW cluster, using wider hot aisles and traditional cooling.
• Liquid-to-air solutions reaching 73 kW per rack, ideal when facility water connections aren't available.
• Liquid-to-liquid systems achieving 73 kW density by integrating with existing chilled water infrastructure.

Purpose-built "AI factories" for new construction:

• 1.8 MW data center halls with optimized chillers and 73 kW rack densities.
• 7.4 MW facilities supporting GB200 NVL72 clusters at 132 kW per rack.
• 7.5 MW facilities for GB300 NVL72 clusters pushing densities to 142 kW per rack.

Critically, this set of designs also includes the Controls Reference Design (CRD1), which integrates building management systems and electrical power monitoring systems (EMPS) with NVIDIA's Mission Control software for unified infrastructure and compute management.

The value extends beyond technical specifications. For operators new to liquid cooling or extreme-density deployments, these designs compress planning cycles from months to weeks. They eliminate guesswork in feasibility analysis. They provide equipment selection lists with components available today, complete with engineering drawings and computational fluid dynamics analysis. Most importantly, they reduce deployment risk by leveraging Schneider Electric's and NVIDIA's combined expertise - lessons learned from actual implementations, not theoretical models.

Intelligent control and sustainability

The Controls Reference Design represents perhaps the most significant innovation in this collaboration. Traditional data centers often operate with separate management systems for power, cooling, and IT equipment. This siloed approach breaks down under AI workloads, where milliseconds matter and equipment operates at thermal limits.

The Controls Reference Design solves this by enabling real-time interoperability between building management systems and NVIDIA Mission Control. Infrastructure and compute resources now communicate directly, creating a unified view of performance, efficiency, and health. The benefits are substantial:

01 Real-time communication between cooling systems, power distribution, and GPU clusters enables proactive responses to changing loads

02 Predictive management of power quality and thermal conditions prevents issues before they impact operations

03 Redundant control architectures for coolant distribution units and electrical panels ensure continuous operation even during component failures

This isn't just about uptime - it's about sustainability. Energy efficiency must be foundational, not optional. AI's computational demands are growing exponentially; if infrastructure efficiency doesn't keep pace, the environmental cost becomes unsustainable. Schneider Electric's designs address Scope 2 and Scope 3 emissions explicitly, using optimized chiller configurations, elevated coolant temperatures, and intelligent load management to minimize energy waste while maximizing performance. Extending hardware lifespan through better thermal management further reduces the embodied carbon of AI infrastructure.

Building the blueprint for the AI age

Looking forward, this collaboration will continue evolving. As NVIDIA develops next-generation GPUs and SuperPODs, Schneider Electric will develop accompanying infrastructure solutions and reference designs. The goal remains constant: keeping data centers AI-ready, future-ready, and planet-ready.

The AI era demands infrastructure designed for its unique challenges. These validated reference designs provide that foundation. They're proven blueprints that translate technical complexity into operational confidence. For data center operators navigating this transition, they represent the shortest path from planning to deployment, with reduced risk and optimized efficiency at every step.