AI Factories, Built Smarter: New Omniverse Blueprint Advances AI Factory Design and Simulation

AI is now mainstream and driving unprecedented demand for AI factories — purpose-built infrastructure dedicated to AI training and inference — and the production of intelligence.

Many of these AI factories will be gigawatt-scale. Bringing up a single gigawatt AI factory is an extraordinary act of engineering and logistics — requiring tens of thousands of workers across suppliers, architects, contractors and engineers to build, ship and assemble nearly 5 billion components and over 210,000 miles of fiber cable.

To help design and optimize these AI factories, NVIDIA today unveiled at GTC the NVIDIA Omniverse Blueprint for AI factory design and operations.

During his GTC keynote, NVIDIA founder and CEO Jensen Huang showcased how NVIDIA’s data center engineering team developed an application on the Omniverse Blueprint to plan, optimize and simulate a 1 gigawatt AI factory. Connected to leading simulation tools such as Cadence Reality Digital Twin Platform and ETAP, the engineering teams can test and optimize power, cooling and networking long before construction starts.

Engineering AI Factories: A Simulation-First Approach

The NVIDIA Omniverse Blueprint for AI factory design and operations uses OpenUSD libraries that enable developers to aggregate 3D data from disparate sources such as the building itself, NVIDIA accelerated computing systems and power or cooling units from providers such as Schneider Electric and Vertiv.

By unifying the design and simulation of billions of components, the blueprint helps engineers address complex challenges like:

• Component integration and space optimization — Unifying the design and simulation of NVIDIA DGX SuperPODs, GB300 NVL72 systems and their 5 billion components.
• Cooling system performance and efficiency — Using Cadence Reality Digital Twin Platform, accelerated by NVIDIA CUDA and Omniverse libraries, to simulate and evaluate hybrid air- and liquid-cooling solutions from Vertiv and Schneider Electric.
• Power distribution and reliability — Designing scalable, redundant electrical systems with ETAP to simulate power-block efficiency and reliability.
• Networking topology and logic — Fine-tuning high-bandwidth infrastructure with NVIDIA Spectrum-X networking and the NVIDIA Air platform.

Breaking Down Engineering Silos With Omniverse

One of the biggest challenges in AI factory construction is that different teams — power, cooling and networking — operate in silos, leading to inefficiencies and potential failures.

Using the blueprint, engineers can now:

• Collaborate in full context — Multiple disciplines can iterate in parallel, sharing live simulations that reveal how changes in one domain affect another.
• Optimize energy usage — Real-time simulation updates enable teams to find the most efficient designs for AI workloads.
• Eliminate failure points — By validating redundancy configurations before deployment, organizations reduce the risk of costly downtime.
• Model real-world conditions — Predict and test how different AI workloads will impact cooling, power stability and network congestion.

By integrating real-time simulation across disciplines, the blueprint allows engineering teams to explore various configurations to model cost of ownership and optimize power utilization.

Real-Time Simulations for Faster Decision-Making

In Huang’s demo, engineers adjust AI factory configurations in real time — and instantly see the impact.

For example, a small tweak in cooling layout significantly improved efficiency — a detail that could have been missed on paper. And instead of waiting hours for simulation results, teams could test and refine strategies in just seconds.

Once an optimal design was finalized, Omniverse streamlined communication with suppliers and construction teams — ensuring that what gets built matches the model, down to the last detail.

Future-Proofing AI Factories

AI workloads aren’t static. The next wave of AI applications will push power, cooling and networking demands even further. The Omniverse Blueprint for AI factory design and operations helps ensure AI factories are ready by offering:

• Workload-aware simulation — Predict how changes in AI workloads will affect power and cooling at data center scale.
• Failure scenario testing — Model grid failures, cooling leaks and power spikes to ensure resilience.
• Scalable upgrades — Plan for AI factory expansions and estimate infrastructure needs years ahead.

And when planning for retrofits and upgrades, users can easily test and simulate cost and downtime — delivering a future-proof AI factory.

For AI factory operators, staying ahead isn’t just about efficiency — it’s about preventing infrastructure failures that could cost millions of dollars per day.

For a 1 gigawatt AI factory, every day of downtime can cost over $100 million. By solving infrastructure challenges in advance, the blueprint reduces both risk and time to deployment.

Road to Agentic AI for AI Factory Operation

NVIDIA is working on the next evolution of the blueprint to expand into AI-enabled operations, working with key companies such as Vertech and Phaidra.

Vertech is collaborating with the NVIDIA data center engineering team on NVIDIA’s advanced AI factory control system, which integrates IT and operational technology data to enhance resiliency and operational visibility.

Phaidra is working with NVIDIA to integrate reinforcement-learning AI agents into Omniverse. These agents optimize thermal stability and energy efficiency through real-time scenario simulation, creating digital twins that continuously adapt to changing hardware and environmental conditions.

The AI Data Center Boom

AI is reshaping the global data center landscape. With $1 trillion projected for AI-driven data center upgrades, digital twin technology is no longer optional — it’s essential.

The NVIDIA Omniverse Blueprint for AI factory design and operations is poised to help NVIDIA and its ecosystem of partners lead this transformation — letting AI factory operators stay ahead of ever-evolving AI workloads, minimize downtime and maximize efficiency.

Learn more about NVIDIA Omniverse, watch the GTC keynote, register for Cadence’s GTC session to see the Omniverse Blueprint in action and read more about AI factories.

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