Siemens and NVIDIA have announced a major expansion of their long-standing partnership, signaling a shift from applied AI experimentation toward system-level deployment across industrial operations. The collaboration focuses on embedding AI into product design, manufacturing execution, semiconductor development, and the infrastructure required to support large-scale AI workloads.
Under the expanded agreement, NVIDIA will supply AI infrastructure, accelerated computing platforms, simulation libraries, and AI models, while Siemens will contribute its industrial software portfolio, automation systems, and domain expertise. The stated objective is to operationalize AI across the full industrial lifecycle, from early design and simulation through production, optimization, and ongoing operations.
A central pillar of the effort is the development of AI-driven manufacturing environments built around continuously updated digital twins. By combining Siemens’ industrial operations software with NVIDIA Omniverse libraries and GPU-accelerated AI infrastructure, factories are designed to analyze real-time operational data, test process changes virtually, and deploy validated adjustments directly to physical systems. Siemens plans to introduce the first fully AI-driven, adaptive manufacturing site at its Electronics Factory in Erlangen, Germany, starting in 2026, with the intent to use it as a reference architecture for broader rollout.
For enterprises, the approach targets measurable operational outcomes: shorter commissioning cycles, reduced production risk, and faster decision-making across engineering and operations. Several industrial customers, including manufacturers and logistics providers, are already evaluating elements of the joint technology stack, suggesting early movement from concept to deployment.
The partnership also extends deeply into simulation and engineering workflows. Siemens will complete GPU acceleration across its simulation portfolio and expand support for NVIDIA CUDA-X libraries and AI-based physics models. This enables larger and more detailed simulations to run faster, improving accuracy while reducing compute time and cost. Over time, the companies aim to move toward generative and autonomous simulation using NVIDIA PhysicsNeMo and open models, allowing digital twins to recommend or execute design and process optimizations with minimal manual intervention.
In semiconductor design, Siemens and NVIDIA are aligning industrial AI methods with electronic design automation. Siemens plans to integrate NVIDIA’s accelerated libraries and AI models across its EDA tools, particularly in verification, layout, and process optimization. The goal is to deliver significant speed improvements in critical workflows while maintaining manufacturability and yield requirements. AI-assisted layout guidance, debugging, and circuit optimization are intended to shorten design cycles and improve predictability for advanced nodes.
Finally, the companies are jointly developing a repeatable blueprint for AI factories capable of supporting high-density computing. This includes coordinated planning for power delivery, cooling, automation, and grid integration — areas that increasingly constrain AI infrastructure deployment. By applying the technologies internally before offering them broadly, Siemens and NVIDIA aim to provide enterprise customers with validated architectures that address scalability, reliability, and energy efficiency.