Cadence debuts Nvidia-powered supercomputer to accelerate enterprise engineering, biotech

“First, the industry is moving from two-dimensional to three-dimensional representations of the world,” said Neil Shah, VP of research and partner at Counterpoint Research. “Designing advanced 3D chipsets, molecules, or LiDAR, camera, and sensor-based autonomous mobility systems for cars and aerospace requires more complex synthetic data generation to train models for advanced simulations.”

The second major trend is the growing importance of AI in processing these advanced use cases, signaling a shift from generative AI to what Shah calls “Physical AI.”

GPUs are seen as the most efficient compute engines capable of managing these workloads at scale. By optimizing its software for GPU architectures, Cadence aims to support enterprises working at the intersection of 3D design and AI.

GPUs also offer greater scalability and flexibility in hybrid and multi-cloud environments, said Manish Rawat, semiconductor analyst at TechInsights.

“They can be deployed across multiple cloud nodes, allowing enterprises to scale simulation workloads on demand without major capital investment in physical infrastructure,” Rawat said. “This aligns with trends toward cloud-native operations.”

Influence on enterprise adoption

Cadence’s move is significant as it offers both large enterprises and startups a pathway into the 3D, AI-accelerated future without the need to invest in their own GPU-scale infrastructure.