From The Deep View, October 14:
Nvidia has figured out a way to build AI factories a lot faster.
These massive data centers bring together billions of dollars of computing equipment into a facility where a ton of different tech, power, cooling, and logistical systems have to work together seamlessly. Managing that kind of complexity is one of Nvidia's strengths.
With chip design, Nvidia architects everything virtually in a simulated environment long before the first manufacturing process starts. And since today's chips and processors have become enormously complex, that orchestration is where the magic happens, in terms of speed, efficiency, and innovation.
Nvidia is applying this same mindset to building AI factories by partnering with Siemens and implementing digital twins, or virtual replicas of real-world environments. This allows Nvidia and its partners, such as hyperscalers and neoscalers, to simulate these elaborate facilities ahead of time and rehearse all of the processes that make them work.
"You’re really building a data center scale computer, along with all the technology, it’s not just about the GPU," Vladimir Troy, Nvidia's VP of AI infrastructure, told The Deep View. "This is the fundamental part that I think people miss about accelerated computing. If you just optimize the GPU, then you have the fastest GPU, and your bottlenecks could be everywhere else."
Digital twins are the key to making it all happen by allowing engineers to simulate a facility's physical behavior. Siemens brings the simulation, automation, and industrial engineering expertise while Nvidia brings the accelerated computing, its Omniverse platform, and the experience with synthetic data.
"You can improve the time-to-token by [roughly] 40% if you’re using digital twins," Ales Alajbegovic, global program manager of industrial metaverse for Siemens, told The Deep View. "If you don’t use digital twins, I don’t even know how you can build this."
In this case, time-to-token is referring to the period from the time you break ground on the facility to the time it's serving its first useful AI query for the public. That metric is critical because AI usage continues to scale up, despite public sentiment turning negative on AI. In the past 18 months, we've gone from roughly 15% of the global population regularly using AI to 19%. And in places like Singapore and UAE, two-thirds of the population are already using it regularly.
All of that usage is putting tremendous strain on today's data centers. Without more data center capacity, the industry faces the risk of AI queries taking much longer to run, the most powerful models being limited to those who can afford to pay a premium, and slowing down some of the most promising uses of AI for science and healthcare. That's why so many of the world's leading cloud and data center companies are building out future capacity, despite limitations on power and component shortages for RAM and data storage....
....MUCH MORE
We saw in the August 2025 earnings call transcript:
....Leading enterprises across industries have adopted Thor, including Agility Robotics, Amazon Robotics, Boston Dynamics, Caterpillar, Figure, Hexagon, Medtronic, and Meta. Robotic applications require exponentially more compute on the device and in infrastructure representing a significant long term demand driver for our data center platform. NVIDIA Omniverse with Cosmos is our data center physical AI digital twin platform built for development of robot and robotic systems. This quarter, we announced a major expansion of our partnership with Siemens to enable AI automatic factories, leading European robotics companies, including Agile Robots, Neurorobotics, and Universal Robots, are building their latest innovations with the Omniverse platform. Transitioning to a quick summary of our revenue by geography....
And before that:
NVIDIA GTC Financial Analyst Q&A March 19, 2024 Video and Transcript
...Rockwell has connected it to their tools for industrial automation. Siemens has connected to their, so it's a bunch of APIs that is physically based and it produces image or articulation and it connects a whole bunch of different environments. And so these APIs are intended to supercharge third party tools. And I'm super delighted to see the adoption across it, particularly in industrial automation. And so those are the five things that we did....