Cadence Design Systems has taken a major leap in AI infrastructure planning by expanding its Reality Digital Twin Platform to include a digital twin of NVIDIA’s cutting-edge DGX SuperPOD with DGX GB200 systems. This integration empowers data center designers to simulate and optimize AI factory deployments with unprecedented accuracy—before a single server is installed.
The platform now supports behaviorally accurate models of NVIDIA’s high-performance compute systems, enabling engineers to design, test, and validate entire AI data centers virtually.
Users can simulate power, cooling, space, and performance constraints to meet specific service-level agreements (SLAs).
The addition is part of Cadence’s ongoing collaboration with NVIDIA, following earlier support for the NVIDIA Omniverse blueprint for AI factory design.
As AI workloads scale from megawatts to gigawatts, traditional data center design methods fall short. The shift to liquid cooling, complex interconnects, and massive compute demands requires a new approach. Cadence’s digital twin platform allows:
- Drag-and-drop modeling of over 14,000 components from 750+ vendors.
- Simulation of failure scenarios, upgrade paths, and long-term performance.
- Visualization of airflow, thermal dynamics, and energy efficiency—all critical for AI-driven operations.
“Rapidly scaling AI requires confidence that you can meet your design requirements with the target equipment and utilities,” said Michael Jackson, SVP of System Design and Analysis at Cadence.
“Creating the digital twin of our DGX SuperPOD is an important step in enabling the ecosystem to accelerate AI factory buildouts,” added Tim Costa, GM of Industrial and Computational Engineering at NVIDIA.
The NVIDIA models are available now upon request and will be included in Cadence’s next software release later this year. The platform will be showcased at the AI Infra Summit in Santa Clara, where Cadence experts will discuss simulation-driven data center operations.
This move positions Cadence as a key enabler in the race to build scalable, efficient, and resilient AI infrastructure. For data center architects, it’s not just about building faster—it’s about building smarter.
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