[Edaily Reporter Song Jae-min] SK Hynix is teaming up with NVIDIA to jointly develop next-generation memory for the construction of an artificial intelligence (AI) factory. The strategy is to move beyond a simple memory semiconductor supply relationship to establish a long-term technology partnership and accelerate efforts to secure a leading position in the next-generation AI infrastructure market.

In addition to jointly developing future AI memory, the companies plan to utilize NVIDIA’s AI infrastructure for actual semiconductor manufacturing and design. This marks a further strengthening of the “AI alliance” between SK Hynix and NVIDIA.

On the 8th, SK Hynix announced that it is strengthening its long-term technology partnership with NVIDIA to develop next-generation memory and innovate semiconductor design and manufacturing for the establishment of a global AI factory.

◇ Joint Development of Next-Generation Memory… Strengthening AI Cooperation
Jensen Huang, CEO of NVIDIA, stated, “AI factories are the engine of the next industrial revolution, and advanced memory is central to their performance,” adding “SK Hynix is an outstanding partner that has played a pivotal role in providing advanced memory technology for NVIDIA’s AI computing platform. We will jointly develop next-generation memory for AI factories and work together to accelerate the expansion of global AI infrastructure, from training frontier models to agentic AI and physical AI.”

SK Group Chairman Choi Tae-won remarked, “This partnership demonstrates the depth of the collaboration between SK Hynix and NVIDIA over the years,” adding, “By jointly developing next-generation memory for AI factories and applying AI to semiconductor design and manufacturing, the two companies will shape the future of AI infrastructure together.”

This long-term partnership ensures a stable supply of advanced memory, taking into account the long development cycles of such products. Based on this, SK Hynix will continue to supply memory that aligns with NVIDIA’s infrastructure roadmap and global demand for AI infrastructure.

Through this partnership, SK hynix will also enter new AI markets pioneered by NVIDIA, including AI infrastructure, personal AI, and physical AI. The two companies plan to jointly develop memory for Vera Rubin, AI supercomputers, Vera CPUs, RTX Spark PCs, and the Jetson Thor robotic computing platform.

The two companies will also collaborate to advance the simulation technologies required for semiconductor development.

SK Hynix is leveraging NVIDIA’s CUDA-X libraries and the NVIDIA PhysicsNeMo framework to increase the processing speed and efficiency of simulation tasks required in the semiconductor design and manufacturing processes. This includes TCAD (Technology Computer-Aided Design) for analyzing semiconductor process characteristics and computational lithography technologies.

The two companies plan to expand this collaboration to the broader fields of Electronic Design Automation (EDA) and simulation. Through this, they aim to establish a collaborative ecosystem involving semiconductor manufacturers, AI computing platform companies, and EDA software firms to drive innovation in the semiconductor development environment.

◇ Jointly Building AI Infrastructure for Semiconductor Design and Manufacturing
“Advancing digital twin technology,” a core foundation for realizing autonomous manufacturing, is also a key objective of this collaboration.

SK hynix is utilizing NVIDIA’s Omniverse and OpenUSD technologies to recreate actual semiconductor factories in a 3D virtual space. The company explains that this enables more precise visualization, analysis, and optimization of complex manufacturing environments.

Additionally, the company is enhancing the operational efficiency of key factory equipment and assets—including autonomous mobile robots (AMRs)—by utilizing cuOpt, a GPU-accelerated decision-making optimization engine, and the Metropolis platform.

The two companies plan to integrate the digital twin environment with existing manufacturing systems and AI-based workflows. Their strategy is to build a "fully autonomous fab" where AI analyzes fab operation data in real time and automates tasks, thereby boosting productivity and operational efficiency while advancing manufacturing decision-making.