Cadence Design Systems, Inc. announced an expanded partnership with NVIDIA to deliver accelerated solutions across agentic AI, physics-based simulation and digital twins to unlock new levels of productivity and accelerate next-generation engineering design flows across semiconductor design, physical AI systems and hyperscale AI factories. By combining Cadence?s leadership in agentic AI-driven design, electronic design automation (EDA) and system design and analysis (SDA) with NVIDIA CUDA-X, AI physics and Omniverse libraries for industrial digital twin solutions, the two companies are redefining engineering productivity across three critical design domains?accelerating innovation at true agent speed. Cadence and NVIDIA are accelerating Cadence EDA and SDA solutions with NVIDIA CUDA-X, AI-physics, Omniverse libraries and the Cadence Millennium M2000 Supercomputer, powered by NVIDIA AI infrastructure.

As part of this expanded collaboration, Cadence will accelerate its wide range of principled solvers and leverage AI physics models to deliver engineering workflows up to 100X speedup. Cadence EDA and SDA customers and partners, including Ascendence, Argonne National Laboratory, Honda R&D, Samsung and SK Hynix are already leveraging Cadence solutions accelerated by NVIDIA to bring accelerated products to market faster. Cadence recently introduced its ChipStack AI Super Agent, which applies agentic AI combined with principled EDA tools to transform semiconductor RTL design and verification.

Building on this foundation, Cadence unveiled AgentStack, a head agent designed to orchestrate all aspects of semiconductor and system design. AgentStack connects Cadence agents with Cadence EDA platforms that leverage NVIDIA Nemotron and run on NVIDIA accelerated computing for orchestrating long-running, multi-agent workflows. As an early partner, NVIDIA is adopting the AgentStack flow in its semiconductor and system design flows and providing real-world feedback that will help Cadence harden and scale AgentStack for broader industry deployment.

Cadence and NVIDIA are extending their collaboration to embedded agentic AI for physical AI, combining the Cadence Physical AI Stack with NVIDIA robotics simulation libraries and accelerated computing to help close the critical ?sim-to-real? gap for robots and autonomous machines. By integrating and accelerating Cadence?s high-fidelity multiphysics simulation and AI workflows with NVIDIA Isaac open-source simulation libraries and Cosmos open-world models, customers gain an end-to-end, agent-orchestrated workflow that links world-model training, accurate physics, large-scale scenario testing and continuous real-world feedback.

The results are then deployed on NVIDIA Jetson robotics and edge AI systems, where a live virtual twin enables continuous monitoring and refinement. The collaboration also extends to AI factories, where Cadence integrates the NVIDIA Omniverse DSX Blueprint to enable next-generation AI factory digital twins that will help customers design, simulate and optimize large-scale Vera Rubin and Grace Blackwell AI factories for training and inference. Using Cadence system analysis and data center simulation tools in combination with NVIDIA DSX libraries and the Omniverse DSX blueprint, customers can explore tradeoffs in GPU power settings, system configurations and cooling architectures before deploying physical systems. In a joint 10-megawatt AI factory use case, modeling GPU operation at a reduced power (MaxQ) demonstrated up to 17% more tokens per watt and billions of dollars of incremental annual revenue per gigawatt for large-scale deployments, increasing net annual revenue and underscoring the value of simulation-driven design for AI factories.

Digital twins of NVIDIA DSX-based AI factories have also demonstrated that combining MaxQ operation with warmer coolant could yield roughly 32% more tokens per watt. By capturing the interactions between IT load, cooling systems, airflow and control logic in a high-fidelity digital twin, operators can safely push their AI factories toward maximum tokens per watt while respecting power and thermal constraints.