AI Factory Digital Twins

AI factories are becoming gigawatt-scale systems—too complex and consequential to build without a plan you can test first. An AI factory digital twin is a high-fidelity virtual representation of an AI factory, including compute, networking, storage, power, and cooling. It can simulate physical conditions, such as thermal behavior and electrical loads, alongside logical compute and network configurations throughout design, validation, deployment, and operations.

While data centers are designed to handle general-purpose computing tasks across fields, an AI factory is specifically optimized for artificial intelligence workloads, with a strong emphasis on AI inference performance and energy efficiency.

Digital twins help teams validate designs and topologies, find issues earlier, accelerate deployment, and improve operational efficiency on day one.

An AI factory digital twin spans the full lifecycle, from early planning and design through live operations. This gives teams a persistent model they can update and return to at every stage.

Simulation and validation continue across five phases:

Plan: Assess site viability and the business case—evaluate site sizing and demand; compare capacity, risk, power availability, and total cost of ownership (TCO); and assess site conditions, such as acoustics, heat flow, and particulates.

Design: Validate layouts and topologies—compare equipment layouts and power, cooling, and networking configurations against site constraints. Catch conflicting requirements early and evaluate design tradeoffs to improve tokens per watt.

Simulated Operations: Rehearse before deployment—simulate commissioning, configuration, and orchestration across IT and facility systems. Validate provisioning and automation workflows before hardware arrives to accelerate time to first token, reduce configuration errors, and build confidence in power utilization.

Procure and Build: Coordinate equipment and construction plans—use the digital twin to improve bill-of-materials accuracy, forecast costs and equipment lead times, and optimize construction scheduling and phasing.

Operate: Monitor, optimize, and improve reliability—connect real-time operational data to the digital twin so teams and integrated partner agents can monitor utilization, anticipate issues, and optimize operations. Test performance, failure, and recovery scenarios and validate new hardware and configuration changes without disrupting production.

Across these phases, connected physical and logical models help facility and IT teams coordinate decisions and evaluate retrofits, technology refreshes, and operational changes.

NVIDIA DSX Sim™ is the high-fidelity simulation layer within the NVIDIA DSX AI Factory Platform—the playbook for AI factory design, deployment, and operations. Physical twins model facility layout, power, cooling, and equipment. Logical twins simulate compute, networking, storage, orchestration, and security.

DSX Sim brings together three components:

• NVIDIA Omniverse™ DSX Blueprint for physical facility twins
• NVIDIA DSX Air for logical infrastructure twins
• NVIDIA DSX SimReady specifications and workflows for simulation-ready 3D assets

Together, they help facility and IT teams work from connected models across the full AI factory lifecycle.

The NVIDIA Omniverse DSX Blueprint provides an open framework for designing physically accurate AI factory digital twins. Using NVIDIA Omniverse libraries with OpenUSD as the shared data layer, teams model facility layout, power topology, cooling systems, and equipment placement. NVIDIA DSX SimReady specifications support validated OpenUSD assets of AI factory equipment, such as racks and coolant distribution units (CDUs), with the geometry, metadata, and connection points needed for simulation. The AI Factory Digital Twin Pipeline Samples guide teams through converting partner CAD and engineering data into validated OpenUSD assets enriched with simulation metadata.

NVIDIA DSX Air provides logical infrastructure simulation within DSX Sim. It creates one-for-one virtual replicas of the AI factory's compute, networking, storage, orchestration, and security stack—including NVIDIA Spectrum-X™ Ethernet switches, NVIDIA ConnectX™ SuperNICs, NVIDIA BlueField™ DPUs, and the NVIDIA Minerva™, Run:ai, and Base Command Manager™ software toolsets. Running in the cloud, DSX Air lets teams design, configure, and pre-validate provisioning scripts, automation workflows, and security policies, as well as ISV ecosystem orchestration, storage, and security solutions before a single server is unboxed.

Teams connect physical and logical twin data through shared workflows so facility engineers and network engineers work from consistent information. Partner ISVs integrate their platforms for tasks such as thermal and fluid simulation, power distribution modeling, systems engineering and design tooling, product lifecycle management, and architecture, engineering, and construction (AEC) workflows. NVIDIA DSX Exchange™ provides the common communication layer for scalable, secure integration of compute, network, energy, power, and cooling signals across IT, OT, and operations systems.

With the digital twin in place, teams run the scenarios that matter most before committing capital or deploying hardware. They can compare layouts, power topologies, and cooling configurations; test capacity limits, failure modes, and redundancy; simulate network topologies and validate automation scripts; and rehearse deployment sequences so day-zero bring-up is faster and more predictable.

After deployment, teams keep the digital twin active. NVIDIA DSX Exchange enables real-time telemetry from the physical factory to update the digital model. Integrated partner operations agents can then use the connected twin to monitor conditions, anticipate issues, and continuously optimize tokens per watt and energy efficiency. The result is a persistent, self-improving model that gets more valuable over time.

NVIDIA DSX Air runs in the cloud—no local GPU is required for simulation. The NVIDIA Omniverse DSX Blueprint can be run locally or in containers, including cloud deployments with browser streaming. The machine hosting the blueprint application must meet its documented GPU and system requirements. Hardware requirements for thermal and fluid simulation depend on the selected software and workload. Refer to the Omniverse DSX prerequisites and simulation software’s documentation for configuration details.

Reviewers can inspect twins and simulation results through Omniverse App Streaming without a local high-end workstation.