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    Home » NVIDIA AI Factories: Omniverse DSX Blueprint for Simulated AI Infrastructure
    Technology & Innovation

    NVIDIA AI Factories: Omniverse DSX Blueprint for Simulated AI Infrastructure

    Art RyanBy Art RyanMarch 17, 2026No Comments3 Mins Read
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    Key Takeaways

    • NVIDIA AI factories are large-scale data centers using the Omniverse DSX Blueprint for design and operation.
    • Digital twins simulate the entire environment, allowing teams to test performance and identify issues before construction.
    • Modular construction accelerates deployment using prefabricated components, simplifying expansion as demand increases.
    • Operational optimization occurs post-construction with AI agents managing performance, cooling, and power usage.
    • The DSX Blueprint standardizes the deployment of NVIDIA AI factories globally, integrating various components for efficiency.

    NVIDIA AI factories are large-scale data centers designed to produce and run artificial intelligence systems. NVIDIA introduced the Omniverse DSX Blueprint to support the planning and operation of these facilities. The announcement was made by NVIDIA CEO Jensen Huang during the GTC event in Washington, D.C. The blueprint provides a framework for designing AI-focused data centers capable of operating at gigawatt-level power capacity. The system combines building design, computing infrastructure, cooling systems, and power management into a single digital environment.

    Digital Twin Simulation for NVIDIA AI Factories

    NVIDIA AI factories use digital twins created with NVIDIA Omniverse and the OpenUSD framework. These digital replicas simulate the full data center environment before construction begins. Engineers can model server layouts, cooling systems, and electrical infrastructure in a virtual space. The simulations allow teams to test performance conditions and identify potential issues. Thermal behavior and energy usage can be evaluated before physical deployment. This approach helps coordinate engineering decisions during the design stage.

    Engineering company Jacobs participates in the planning process. Teams assemble SimReady OpenUSD models of infrastructure components. Equipment from companies such as Siemens, Schneider Electric, Trane Technologies, and Vertiv is included in the digital environment. Product lifecycle management systems from PTC allow teams to collaborate on the models and update designs in real time.

    Infrastructure and Modular Construction in NVIDIA AI Factories

    NVIDIA AI factories rely on modular construction systems to accelerate deployment. Prefabricated infrastructure modules are produced by partners such as Bechtel and Vertiv. These factory-built components allow data centers to be assembled more quickly than traditional construction. Modular systems also support easier expansion as computing demand grows.

    The blueprint supports facilities ranging from around 100 megawatts to multi-gigawatt AI data centers. The system integrates power delivery, liquid cooling systems, and high-performance computing hardware within a unified architecture.

    Operational Optimization for NVIDIA AI Factories

    After construction, the digital twin continues operating as a monitoring platform. Operational data from the facility feeds into the simulation environment. AI agents from companies including Phaidra and Emerald AI analyze this data. The agents adjust cooling systems, power usage, and workload distribution to maintain stable operations.

    The DSX architecture includes three components. DSX Boost focuses on maximizing GPU performance per watt. DSX Flex coordinates power consumption with energy grids and renewable sources. DSX Exchange connects operational technology systems with NVIDIA software platforms.

    NVIDIA developed the DSX Blueprint with partners across the infrastructure, energy, and software sectors to support standardized deployment of NVIDIA AI factories worldwide.

    Source: https://blogs.nvidia.com/blog/dsx-air-simulation-ai-factories/

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    Art Ryan

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    August 25, 2026

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