Cadence accelerates digital twin–driven data centre AI modernisation with HPE
Cadence has announced an expansion of its collaboration to accelerate digital twin–driven data centre modernisation with HPE, enabling customers to improve planning, optimisation, and lifecycle operations for next-generation AI and high-performance computing (HPC) infrastructure. The collaboration combines the Cadence Reality Digital Twin Platform, which virtualises data centre environments using AI, HPC, and physics-based simulation to significantly optimise end-to-end computational throughput, with HPE’s sustainable data centre modernisation services. The standardisation of the platform within HPE’s AI-focused modular data centre, AI Mod POD, also improves total cost of ownership, speed of deployment, and increase operational efficiency.
As data centres evolve to support increasingly power-dense AI workloads and advanced cooling architectures, operators must modernise quickly while meeting sustainability, regulatory, and service-level objectives. Together, Cadence, NVIDIA and HPE will deliver scalable, energy-efficient data centre blueprints—from edge to cloud—helping customers de-risk decisions before physical deployment, unlock stranded capacity, and maintain optimal performance as requirements change.
The Cadence Reality Digital Twin Platform enables customers to create high-fidelity digital replicas of entire data centres and campuses by dragging and dropping vendor-provided digital models that simulate the physical behaviour of their real-world counterparts. These predictive models help data centre operators optimise energy efficiency, capacity, and resiliency from initial design through day-to-day operations.
Those looking to improve their data centre architecture can do so, working with HPE’s data centre design and engineering team that incorporates the Cadence Reality Digital Twin Platform to build an engineering-accurate, physics-based layer that allows teams to evaluate design tradeoffs earlier and continuously refine operations against real-world constraints.
