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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchCadence and NVIDIA are combining digital-twin and simulation technologies to help engineers estimate how an AI data center’s power, cooling, airflow and thermal systems will behave before deployment. The software supports facility planning and operational what-if analysis; it is not a consumer power meter or a guarantee of a specific energy saving.
What the Cadence–NVIDIA collaboration does
Cadence’s Reality Data Center Digital Twin Platform works with NVIDIA Omniverse and DSX technologies to create a virtual representation of an AI data center. Depending on the model, that representation can include compute systems, power settings, cooling architecture, airflow, and thermal and fluid behavior. Engineers can examine proposed configurations and operating or failure scenarios without first making those changes in a physical facility.
The goal is to give teams a way to reason about energy use and reliability as they plan high-density AI facilities. Cadence provides the digital-twin and simulation layer; NVIDIA contributes Omniverse/DSX technologies and AI-system models. Cadence describes the Reality Data Center Digital Twin Platform, while NVIDIA describes the collaboration and its AI-factory context.
How simulation can predict facility power demand
Rather than reading electricity use from an already operating building, engineers provide or vary design and operating inputs in a simulation. These can include GPU power settings, system configurations, workloads and cooling architectures. The digital twin then lets them inspect predicted effects across power, thermal and fluid behavior, including how airflow and cooling choices affect equipment.
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This makes the tool useful for comparing scenarios: for example, whether a change in system configuration or cooling design could better meet servers’ needs, or how a facility might behave under a planned operating condition. It supports decisions during design, deployment and operations, including planning for failure cases. The output is a model-based estimate, not a guarantee that real-world consumption will match a prediction exactly.
What the published figures do—and do not—show
- Up to 30% energy-efficiency improvement: Cadence’s 2024 Reality Digital Twin Platform release says the platform can “significantly improve data center energy efficiency by up to 30%.” This is Cadence’s stated platform claim, not an independently verified result for every facility or a universal forecast. Cadence’s 2024 announcement.
- More than 800 racks and 200+ NVIDIA DGX H100 systems: Cadence’s 2025 Corporate Impact Report describes NV5 work on data centers with sectors exceeding 800 racks and 200+ AI DGX H100 systems. Those figures describe the scale of facilities in the report; they are not a measured power-saving result. Cadence’s Corporate Impact Report page.
- GB300 NVL72 and Vera Rubin models: NVIDIA said in 2026 that Cadence was integrating simulation-ready models of the GB300 NVL72 system and collaborating on Vera Rubin models for thermal and fluid simulation. This indicates work on models for those systems; it does not by itself establish a measured facility-level efficiency gain. NVIDIA’s collaboration announcement.
Who is using the approach?
NV5 is a documented engineering user. In Cadence’s 2025 Corporate Impact Report, NV5 COO Andrew Chang, Buildings & Technology, said: “Through simulation tools, we are able to better engineer the use of power, air flow, and focus on satisfying individual servers within a data center and reduce wasted energy.” The report also describes NV5 work involving high-density facilities with the rack and DGX H100 system counts noted above. This is evidence of engineering use, not a published independent evaluation of the platform’s results.
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- Easy equipment configuration: Fully adjustable mounting rails and numbered U positions, with square holes for easy equipment mounting with top and bottom punchout panels for easy cable access
- Durability: Made of high quality cold rolled steel holds up to 110lb (50kg) (Easy Assembly Required)
- PCI & HIPPA and EIA/ECA-310-E compliant
Does a digital twin reduce AI data-center energy use?
Simulation can help teams identify design or operating choices that may use power more effectively—for example, by examining how cooling and airflow serve individual systems. The software itself does not establish that a facility will consume less electricity. Actual results depend on the physical design, equipment, workload, operating decisions and how well the model represents the facility. Cadence’s up-to-30% figure should therefore be read as a vendor claim about potential platform impact, not as a guaranteed or independently measured saving for a particular data center.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why model fidelity and scope matter
A useful prediction depends on what the model represents and the decision being tested. A model that includes system power settings alongside thermal and fluid behavior can help teams study interactions between computing equipment and cooling. The collaboration’s stated scope reaches beyond a single server to facility planning and operations, but the accuracy of any particular estimate depends on the inputs and model detail available for that project.
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For a facility team, the practical questions are whether the simulation represents the relevant GPU systems and workload, whether cooling and airflow are modeled at the needed level, and whether the scenario reflects the intended design or operating condition. A virtual representation helps compare options before changes are made; it should be interpreted as decision support, not as a substitute for validating real operating performance.
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