Dassault Systèmes and NVIDIA announced a long-term partnership. They will work together to create an industrial AI platform. This platform will use Dassault Systèmes’ Virtual Twin technologies. It will also integrate NVIDIA’s powerful AI tools, open models, and fast software libraries. The 3DEXPERIENCE World conference and goal to raise industries’ utilization of high, performance computing, digital twin simulations, and AI, driven workflows at a massive scale to an entirely new level.
The collaboration will enable the creation of “Industry World Models” that are science-backed and consist of advanced AI models based on physics and the real world, which can design, simulate, and control large systems in fields such as biology, materials science, engineering, and manufacturing. The new platform will be developed using Dassault Systèmes’ 3DEXPERIENCE agentic platform and NVIDIA’s computing infrastructure, which will allow the creation of virtual companions that use deep context and predictive knowledge to accelerate decision-making and innovation.
What This Collaboration Means
Fundamentally, the collaboration between Dassault Systèmes and NVIDIA involves the combination of two highly successful technologies into one streamlined process for industrial AI:
Virtual Twin Technology: This involves the creation of digital models of complex physical systems that can be analyzed by engineers before they are actually created in the physical world.
AI Infrastructure Acceleration: This involves the integration of NVIDIA’s CUDA-X libraries, Omniverse platform, and Nemotron open models to deliver high-performance simulation, AI inference, and physics-based modeling.
AI “Virtual Companions”: These are intelligent agents that assist users in data exploration, design validation, and the automation of routine analytical tasks in the 3DEXPERIENCE platform.
The platform is intended to be deployable anywhere in the world through Dassault Systèmes’ OUTSCALE cloud infrastructure, ensuring that customers have the ability to run complex AI and digital twin workloads with high data privacy, security, and compliance assurances.
Transforming the Computing and Data Center Landscape
This partnership represents a significant shift in the Computing and Data Center industry. It shows how AI and high-performance computing (HPC) work together with engineering, simulation, and operational tasks.
1. Pushing the Boundaries of High-Performance Computing for AI Workloads
The computing backbone of this initiative depends on NVIDIA’s accelerated computing and AI systems. Deploying these capabilities to support virtual twins and large-scale simulations places heavy demands on computation, memory bandwidth, and parallel processing – all areas where HPC and GPU-accelerated data centers excel.
In practice, this means data centers supporting this platform must provide:
Massive GPU resources for AI inference and simulation.
Low-latency interconnects for distributed model training and execution.
Scalable storage and bandwidth to handle extensive engineering datasets.
Meeting these needs helps data center operators and cloud providers build AI-ready infrastructures for industrial clients. This shift broadens its scope beyond traditional hyperscale workloads. It now encompasses physics-based simulation and digital twin co-processing.
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2. Accelerating Virtual Twin and AI Adoption Across Industries
Virtual twin technologies need a lot of computing power. They run simulations that include multiple physics. They model behavior under different conditions. They also update models using real-time data. Integrating NVIDIA’s AI libraries with Dassault Systèmes’ modeling helps businesses:
Run simulations faster and more accurately.
Use AI to explore scenarios not feasible with manual engineering alone.
Scale simulations across cloud or hybrid environments for real-time insights.
This change highlights a wider trend in computing. AI and simulation tasks are coming together. Data centers need to adapt to manage these combined loads effectively.
3. Enabling Next-Generation Engineering and Scientific Research
The partnership’s emphasis on science-validated world models extends well beyond manufacturing into areas like biology and materials science — domains where complex computational models can accelerate discovery and reduce time to innovation. These workloads often involve high-dimensional simulations and training of large AI models, pushing the boundaries of data center architectures.
For computing infrastructure, this means:
Increased need for heterogeneous computing resources (GPUs, CPUs, AI accelerators).
High I/O throughput and data locality optimization.
The ability to support multi-tenant, cloud-native AI simulation frameworks that can scale on demand.
These requirements are already driving data centers to become specialized AI and simulation centers, effectively blurring the lines between traditional enterprise computing and scientific HPC environments.
Business Impacts Across Computing and Data Center Sectors
New Market Opportunities for Cloud and HPC Providers
Enterprises that want to use industrial AI and virtual twin platforms need strong infrastructure support. Cloud providers and HPC specialists can benefit from:
Managed AI simulation services.
Tailored GPU clusters for engineering workloads.
AI factory orchestration for continuous model training and inference.
You can tell these offerings apart by their performance, security, and how well they fit into industry-specific workflows.
Enhanced Productivity and Competitive Advantage
Using a combination of AI and virtual twins allows companies to streamline engineering cycles, lower prototyping costs, and enhance product reliability. For industries such as automotive, aerospace, and materials science, this speed, up can be converted directly into a shorter time, to, market and highly competitive product development.
Driving Innovation in AI-First Engineering
By having AI companions and physics-informed models integrated into their daily workflows, engineers and designers can spend more time on creative problem-solving and less time on repetitive simulation setup and tuning. This is the paradigm of AI helping human decision-making at scale, which is characteristic of agentic AI and represents the elevation of computing from a tool to an innovation partner.
Security, Compliance, and Data Sovereignty
Because Dassault Systèmes’ OUTSCALE cloud includes built-in data governance and sovereignty protections, organizations in regulated industries (e.g., aerospace, pharmaceuticals) can run sensitive simulations without risking IP leakage or compliance violations — a crucial consideration for enterprise data centers.
Conclusion
The Dassault Systèmes–NVIDIA partnership represents a significant leap forward in how industrial AI — grounded in physics and real-world data — is built and scaled. By deeply integrating virtual twin technologies with accelerated computing, the collaboration not only reshapes computing and data center requirements but also broadens the scope of how businesses leverage AI for simulation, design, and operational excellence.