Industrial sectors face pressure to digitise, prompting Siemens and NVIDIA to integrate AI infrastructure directly into physical workflows.

The disparity between IT and operational technology (OT) remains a costly friction point for manufacturing. While data centres scale rapidly, factory floors often run on rigid legacy systems. Siemens and NVIDIA are expanding their partnership to address this mismatch, effectively attempting to build an operating system for industrial AI.

Beyond static models for industrial sectors

Digital twins – virtual replicas of physical assets – are standard in engineering but usually lack real-time connection to the hardware they represent. They often sit in the design studio, not on the production line. This updated alliance intends to change that dynamic by using generative AI to make these models active rather than static.

“Generative AI and accelerated computing have ignited a new industrial revolution, transforming digital twins from passive simulations into the active intelligence of the physical world,” said Jensen Huang, Founder and CEO of NVIDIA.

NVIDIA provides the computing stack and blueprints, while Siemens brings the domain hardware and software. The objective is a closed loop where a factory can analyse its own data, simulate a fix, and implement it without downtime. This allows operators to test improvements virtually and turn validated insights into operational changes on the shopfloor.

The companies selected the Siemens Electronics Factory in Erlangen, Germany, to test the concept. From 2026, this site will allegedly operate as a “blueprint” for AI-driven production.

The architecture relies on an “AI brain” combining software-defined automation with NVIDIA Omniverse libraries. Early evaluation by companies in the industrial sectors is underway at Foxconn, HD Hyundai, KION Group, and PepsiCo. The intent is to standardise this approach to move away from bespoke, site-specific setups toward a repeatable model suitable for global deployment.

Infrastructure for high-density compute

Operational speed matters upstream in chip design too. Siemens is embedding NVIDIA’s CUDA-X libraries into its Electronic Design Automation (EDA) tools.

Verifying chip designs is a massive compute sink. By shifting these workloads to GPU-accelerated environments, the partnership targets performance gains between 2x and 10x. The updates include AI-assisted capabilities such as layout guidance and debugging, aiming to shorten the cycle from architecture to silicon.

Compute density brings thermal and power challenges. The collaboration includes a joint effort to design factories that can handle the energy load of AI workloads without compromising efficiency. This involves aligning NVIDIA’s infrastructure roadmap with Siemens’ power distribution and cooling technologies.

“Together, we are building the Industrial AI operating system — redefining how the physical world is designed, built and run — to scale AI and create real-world impact,” said Roland Busch, President and CEO of Siemens AG.

Both companies are implementing these tools internally before a broader rollout. NVIDIA is using Siemens’ tools to optimise its operations, while Siemens applies NVIDIA’s compute stack to its own workloads. For prospective buyers, this internal validation offers a proof point often missing in vendor announcements.

Executives reviewing this development for the industrial sectors should look at their data estate. An “AI brain” requires clean and structured operational data. If the OT network is air-gapped or unstructured, the advanced simulation described here is impossible. Start by auditing data availability at the edge.

See also: Sony: How eRedCap enables the 4G to 5G IoT transition

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