Industrial technology giant Siemens and chip maker NVIDIA announced a major expansion of their strategic partnership on January 6, aiming to integrate artificial intelligence across manufacturing and industrial operations through what they describe as an “industrial AI operating system.”
The collaboration combines NVIDIA’s AI computing platforms with Siemens’ industrial hardware, software, and engineering expertise to develop solutions spanning product design, manufacturing optimization, and operational management. The partnership targets applications across multiple industries, from semiconductor fabrication to factory automation.
Strategic Vision and Leadership Perspective
Roland Busch, president and CEO of Siemens AG, framed the partnership as transformative for industrial operations.
“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,” Busch said. “By combining NVIDIA’s leadership in accelerated computing and AI platforms with Siemens’ leading hardware, software, industrial AI, and data, we’re empowering customers to develop products faster with the most comprehensive digital twins, adapt production in real time, and accelerate technologies from chips to AI factories.”
Jensen Huang, founder and CEO of NVIDIA, positioned the collaboration within broader industrial transformation.
“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,” Huang said. “Our partnership with Siemens fuses the world’s leading industrial software with NVIDIA’s full-stack AI platform to close the gap between ideas and reality, empowering industries to simulate complex systems in software, then seamlessly automate and operate them in the physical world.”
Technical Implementation and Resource Commitment
Under the expanded partnership, NVIDIA will provide AI infrastructure, simulation libraries, models, frameworks, and implementation blueprints. Siemens will contribute hundreds of industrial AI specialists along with its portfolio of industrial hardware and software systems.
The technical architecture centers on AI-accelerated solutions across the complete lifecycle of products and production processes, intended to enable faster innovation, continuous optimization, and more resilient manufacturing operations.
First AI-Driven Adaptive Manufacturing Site
The companies plan to build what they describe as the world’s first fully AI-driven adaptive manufacturing sites, beginning in 2026 with the Siemens Electronics Factory in Erlangen, Germany, serving as the initial implementation blueprint.
The Erlangen facility will employ an “AI Brain” powered by software-defined automation and industrial operations software, combined with NVIDIA Omniverse libraries and NVIDIA AI infrastructure. This system will continuously analyze digital twins of the factory, test improvements virtually, and implement validated changes on the physical production floor.
According to the companies, this approach aims to accelerate decision-making from design through deployment, increasing productivity while reducing commissioning time and operational risk.
Early Adopter Interest
Several major corporations are evaluating the partnership’s capabilities, including Foxconn, HD Hyundai, KION Group, and PepsiCo, according to the announcement. The companies did not disclose specific implementation timelines or financial commitments from these potential customers.
Semiconductor and AI Factory Focus
The partnership includes specific initiatives targeting semiconductor design and AI data center construction—two areas critical to AI infrastructure development.
Siemens will integrate NVIDIA CUDA-X libraries, PhysicsNeMo, and GPU acceleration across its electronic design automation (EDA) portfolio, focusing on verification, layout, and process optimization. The companies project performance improvements of 2-10x in key workflows, building on NVIDIA’s existing use of Siemens tools in its own chip design processes.
The collaboration will also develop standardized blueprints for next-generation AI factories, addressing high-density computing requirements for power, cooling, and automation while optimizing efficiency across planning, design, deployment, and operations phases.
GPU Acceleration Across Simulation Portfolio
As part of the expanded partnership, Siemens will complete GPU acceleration across its entire simulation portfolio and expand support for NVIDIA CUDA-X libraries and AI physics models. This integration aims to enable customers to run larger, more accurate simulations with increased speed.
The combined infrastructure bridges NVIDIA’s AI platform capabilities and partner ecosystem with Siemens’ expertise in power infrastructure, electrification, grid integration, automation, and digital twin technology.
Mutual Implementation Strategy
The partnership includes a commitment from both companies to implement technologies on their own systems before scaling them across industries—a strategy designed to create validated proof points for customers.
NVIDIA will assess Siemens offerings to optimize its own operations, while Siemens will collaborate with NVIDIA to accelerate its workloads and integrate AI throughout its customer portfolio. This mutual adoption approach aims to demonstrate concrete value and scalability before broader market deployment.
Industry Context and Implications
The expanded partnership reflects intensifying competition to provide AI-powered industrial solutions as manufacturers seek to optimize operations, reduce costs, and improve sustainability. Digital twin technology—virtual replicas of physical systems—has become increasingly central to industrial planning and operations, with AI integration promising more dynamic and responsive capabilities.
However, the announcement did not include specific financial terms, detailed implementation timelines beyond the 2026 Erlangen factory target, or independent validation of the projected performance improvements. The companies also did not disclose how they will address potential challenges in scaling complex AI systems across diverse industrial environments with varying technical infrastructure and operational requirements.
