Creating Gigawatt AI Factory Blueprints with Digital Twins

The data centre industry is scaling at an unprecedented rate to meet the colossal demands of AI. Yet, as data centre facilities push rapidly into gigawatt territory, the traditional methods of designing, building and operating infrastructure face a critical bottleneck. The sheer density and thermal output of next-generation hardware mean the margin for physical trial and error has essentially vanished.

To circumvent these physical limitations, the industry is increasingly turning to the virtual world. By creating highly accurate, simulation-ready replicas of power, cooling and control systems, operators can stress-test the AI data centres of tomorrow before laying a single brick.

This shift from physical guesswork to digital certainty was heavily underscored at the recent NVIDIA GTC 2026 event, where energy technology leader Schneider Electric, industrial software specialist AVEVA and NVIDIA detailed a highly integrated approach to the lifecycle digital twin.

“As AI workloads scale in both size and complexity, the margin for error in data centre design becomes incredibly small,” notes Manish Kumar, Executive Vice President of Secure Power & Data Centers at Schneider Electric. “Delivering AI at scale requires tightly integrated electrical, cooling and digital architectures that can support both unprecedented performance demands while maintaining peak energy efficiency.”

Simulating success before the build

The core of this new digital-first methodology lies in a lifecycle digital twin architecture designed to accelerate the deployment of AI factories while maximising token revenue per megawatt. Co-developed by AVEVA and NVIDIA, this architecture is now embedded throughout the NVIDIA Omniverse DSX Blueprint and ecosystem.

Rather than treating the data centre as a collection of isolated systems, this approach brings all components into a unified virtual environment. Once a system architecture is assembled within NVIDIA Omniverse, AVEVA’s software executes multi-domain simulations to validate how the facility will behave under realistic, high-stress conditions.

These computational models test power distribution, thermal dynamics, airflow performance and control systems simultaneously. Crucially, they enable engineers to rapidly evaluate multiple scenarios across a wide range of loads and environmental conditions.

“By combining advanced software, digital twins and validated reference designs, operators can simulate and optimise infrastructure before a single rack is deployed,” Manish explains. “This approach reduces risk, accelerates deployment and ensures the efficiency and resilience needed to power the next generation of AI factories.”

This domain-specific simulation directly targets a critical metric for AI operators: time-to-token. By finalising system verification in a digital environment, operators can drastically reduce engineering cycles and improve deployment accuracy in the physical world.

“Gigawatt-scale AI factories demand a fundamentally new class of energy-efficient and highly predictable infrastructure,” says Vladimir Troy, Vice President of AI Infrastructure at NVIDIA. “Together, NVIDIA and Schneider Electric are providing the power, cooling, and digital twin architectures needed to accelerate time-to-token for our customers worldwide.”

Validating next-generation rack architecture

The practical output of these advanced digital twins can be seen in the new reference designs required to house the latest silicon. A prime example is the newly unveiled reference design for the NVIDIA Vera Rubin NVL72 racks.

Validated entirely through ETAP models for electrical system design and EcoStruxure IT Design computational fluid dynamics (CFD) for layout and airflow, the design provides a roadmap for powering and cooling ultra-dense rack-scale systems. The digital validation addresses several stringent infrastructure requirements, including:

• Elevated Power and Cooling: Facilitating power distribution with an increased supply voltage of 480 VAC, alongside allowing a higher TCS loop supply temperature of 45°C to enhance overall cooling efficiency.
• Clustered Architecture: Supporting an IT room layout where clusters of AI racks share centralised networking, storage, and CPU support. This keeps NVIDIA rack-scale systems physically close while allowing separate, higher voltages for the GPU racks to optimise power delivery.
• Performance Optimisation: Designing facilities to accommodate various operating points of GPU racks (both MaxP and MaxQ). Operating at MaxQ, for instance, overrides power constraints to achieve more tokens per watt, optimising computing performance through built-in redundancy.

Because these configurations have already been verified in a digital twin environment, data centre owners can scale dense architectures rapidly without compromising system stability.

Autonomous operations and agentic AI

The utility of the digital twin does not end once the data centre is built – it transitions into a vital tool for real-time operations and maintenance. Managing a gigawatt-scale facility generates an overwhelming amount of telemetry. Interpreting system-level alarms to identify root causes and deploy corrective actions remains a longstanding operational challenge.

To address this, Schneider Electric is bridging the gap between digital monitoring and autonomous action through the testing of the NVIDIA Nemotron model. This open model is being used to power a new agentic AI capability for data centre alarm management services.

By leveraging real-time IoT data from across the facility's multiple systems, the agentic AI acts as a highly advanced diagnostic layer. It autonomously analyses the data, diagnoses the root cause of an alarm, and recommends specific actions using a suite of integrated tools. Designed to work alongside expert human technicians, this autonomous layer reduces unnecessary operational dispatches and delivers faster, more consistent issue resolution.

This move toward software-defined operations represents a natural evolution for the sector. From standardising simulation-ready 3D assets via the Alliance for OpenUSD to integrating real-time thermal and electrical modelling, the industry is laying a comprehensive foundation. 

Ultimately, the gigawatt AI factories of the future will be defined not just by the hardware they house, but by the digital twins that ensure they run smoothly long before they are ever switched on.