Digital twins in data centres: a collaboration between Cadence and HPE

Cadence and HPE team up to modernise data centres with digital twin technology, aiming to strengthen efficiency and sustainability for AI and HPC infrastructure.

Cadence Design Systems has announced an expansion of its collaboration with Hewlett Packard Enterprise (HPE) focused on digital twin–driven modernisation in data centres. The work is intended to support planning, optimisation, and lifecycle operations for AI and high-performance computing (HPC) infrastructure.

The collaboration combines the Cadence Reality Digital Twin Platform—which is used to virtualise data centre environments through AI, HPC, and physics-based simulation techniques—with HPE’s data centre modernisation capabilities. This integration is being applied within HPE’s AI-focused modular data centre system, the AI Mod POD, to evaluate deployment and operational scenarios.

The effort also involves collaboration with NVIDIA, with the goal of supporting energy and infrastructure planning approaches that can be assessed before physical deployment, from edge to cloud environments.

In the context of increasing power density from AI workloads and growing requirements for cooling and efficiency, digital twin approaches are being used to support data centre design and operations.

Key areas of focus include:

  • Design optimisation: Using high-fidelity digital twins to plan AI- and HPC-ready data centres that meet power, space, cooling, and sustainability constraints.
  • Scenario evaluation: The Cadence Reality DC Elements Design Library is used to assess different AI Mod POD deployment configurations and related technology options.
  • Predictive modelling: Simulation of power and cooling behaviour to help evaluate utilisation and system changes before implementation.
  • Operational analysis: “What-if” scenarios are used to support longer-term capacity planning and energy management.

The integration of Cadence’s digital twin tools with HPE’s data centre systems is intended to support analysis and decision-making during both the design phase and ongoing operations of AI infrastructure deployments.

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