Schneider Electric, AVEVA, Nscale and Start Campus bring the NVIDIA Omniverse DSX Blueprint into a Live AI Factory Environment

Global energy technology leader Schneider Electric, industrial software leader AVEVA, full-stack AI cloud platform Nscale, and data center developer and operator Start Campus, today announced one of the first pilots of an end-to-end digital twin developed using the NVIDIA Omniverse DSX Blueprint, part of NVIDIA DSX Sim.

The Immersive Twin pilot marks a significant step toward bringing Schneider Electric’s digital twin technology, powered by AVEVA software, into real-world AI infrastructure, allowing Nscale to accelerate token production and optimize token efficiency.

The deployment is focused on Nscale’s operations within the SIN01 data center on the 1.2 GW SINES Data Campus in Portugal, developed by Start Campus. As one of Europe’s largest and most sustainable AI factories, the SINES Data Campus is designed to support successive generations of AI infrastructure. It will soon become home to more than 66,000 NVIDIA Rubin GPUs, representing one of the largest AI deployments in the region. By combining engineering and operational data with advanced power and cooling simulations and workload requirements, the new Schneider Electric and AVEVA Immersive Twin provides a unified view of data center performance to help identify opportunities to optimize infrastructure before changes are implemented on-site.

Using the digital twin, Nscale expects to achieve faster time to token, greater token efficiency, enhanced operations, increased flexibility, and reduced risk when updating or modifying its technology stack.

“Working with Start Campus, Schneider Electric and NVIDIA, we’re using digital twins to model how AI data centers operate in the real world — from power and cooling to token delivery,” said David Power, Chief Technology Officer at Nscale. “This gives us a clearer view of where full-stack optimizations can be realized before changes are made on the ground. It’s about using intelligence and simulation to build and operate AI infrastructure more efficiently, and sustainably at scale.”

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