Schneider Electric teams with NVIDIA to develop validated blueprints to design, simulate, build, operate and maintain gigawatt-scale AI Factories

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Image - Schneider Electric teams with NVIDIA to develop validated Blueprints to design, simulate, build, operate and maintain gigawatt-scale AI FactoriesImage - Schneider Electric teams with NVIDIA to develop validated Blueprints to design, simulate, build, operate and maintain gigawatt-scale AI Factories GNW

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SAN JOSE, March 16, 2026 (GLOBE NEWSWIRE) —

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  • New NVIDIA Vera Rubin reference design, co-developed with NVIDIA, provides validated roadmap for powering and cooling latest NVIDIA rack-scale systems
  • AVEVA, the industrial software company owned by Schneider Electric, and NVIDIA develop lifecycle digital twin architecture in NVIDIA Omniverse for large-scale AI factories to maximize token revenue per megawatt
  • Schneider Electric testing of NVIDIA Nemotron model for agentic AI marks important step toward the next generation of autonomous, software‑defined operations

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(NVIDIA GTC 2026) — Schneider Electric, a global energy technology leader, in collaboration with NVIDIA and industrial software leader AVEVA, has today announced key advancements in designing, simulating, building, operating and maintaining the next generation of AI data center infrastructure during NVIDIA GTC in San Jose. They include a new NVIDIA Vera Rubin reference design that validates power and cooling for the latest NVIDIA rack-scale architectures, integration of advanced digital twin capabilities within the NVIDIA Omniverse DSX Blueprint and ecosystem, and early testing of agentic AI for data center alarm management services using NVIDIA Nemotron open models.

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The company’s announcements further strengthen Schneider Electric and NVIDIA’s existing collaboration and establish a comprehensive foundation for developing AI Factories built for gigawatt-scale and efficiency.

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New NVIDIA Vera Rubin Reference Design

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The newly unveiled AI reference design is one of the first created for the NVIDIA Vera Rubin NVL72 racks. The validated reference design covers power and cooling and is integrated with Schneider Electric’s controls reference designs. Importantly, the design addresses important infrastructure requirements and considerations for NVIDIA’s latest rack-scale systems:

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  • Enables new power distribution with increased supply voltage of 480 VAC
  • Allows higher TCS loop supply temperature of 45°C for enhanced efficiency
  • Supports new IT room architecture with clusters of AI racks sharing centralized networking, storage, CPU, and support racks. This allows every NVIDIA rack-scale system to remain physically close together while allowing separate, higher voltage for the GPU racks to enable larger clusters and optimize power delivery
  • Maximizes token performance by designing data centers to accommodate various operating points of GPU racks (both MaxP and MaxQ). Operating at MaxQ can achieve more tokens per watt to override any power constraints and optimize computing performance through redundancy. Overall, the reference design enables more tokens per watt when incorporating NVIDIA’s MaxQ operating point.

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The reference design is validated with ETAP models for electrical system design and ITD CFD models for layout and air flow.

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New AVEVA Lifecycle Digital Twin Architecture for Gigawatt-Scale AI Factories

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Additionally, AVEVA, a global leader in industrial software owned by Schneider Electric, together with NVIDIA, has announced a new lifecycle digital twin architecture that maximizes GPU efficiency and accelerates the deployment of AI factories at speed and scale. Schneider Electric is committed to creating SimReady assets and digital twins through NVIDIA Omniverse, supported by AVEVA’s advanced software. With this announcement, AVEVA’s engineering and operations software is now embedded throughout the NVIDIA Omniverse DSX Blueprint and ecosystem. It is projected to accelerate time-to-token through domain-specific simulations, digital visualization and collaborative design tools that will drive significant engineering optimization.

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