NVIDIA and SK Hynix: Partnering on Next‑Gen Memory for AI

2026-06-09 · Source: AI Magazine · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

NVIDIA and SK Hynix announced a multi-year partnership on June 09, 2026, to co-develop next-generation memory and apply AI to semiconductor design and manufacturing, supporting the global expansion of AI factories. This agreement ensures advanced memory supply for NVIDIA's AI computing platforms, including Vera Rubin AI supercomputers, Vera CPUs, RTX Spark-powered PCs, and Jetson Thor robotic computing platforms. The collaboration also involves using NVIDIA CUDA-X libraries and PhysicsNeMo to accelerate chip design, fabrication, and semiconductor simulations, aiming to reduce verification time and improve yield prediction. Furthermore, SK Hynix is developing fab digital twins with NVIDIA Omniverse and OpenUSD to optimize autonomous manufacturing operations, enhancing throughput and safety. Broader collaborations extend to SK Telecom building a gigawatt-scale AI cloud with NVIDIA DSX by 2027 and Doosan Group partnering on physical AI and robotics.

Key takeaway

For AI Architects and Directors of AI/ML planning future infrastructure, this partnership signals a critical shift towards integrated hardware-software co-design and AI-driven manufacturing. You should prioritize strategic vendor partnerships that offer advanced memory solutions and utilize AI for optimizing your semiconductor supply chain and fab operations. Consider exploring digital twin technologies for autonomous manufacturing to enhance throughput and reduce downtime in your AI factory deployments.

Key insights

Strategic partnerships are crucial for scaling AI infrastructure through co-development of advanced memory and AI-driven manufacturing.

Principles

• Advanced memory is essential for AI performance.
• AI accelerates semiconductor design and fabrication.
• Digital twins optimize manufacturing operations.

Method

Companies apply AI to chip design using NVIDIA CUDA-X libraries and PhysicsNeMo for simulations. Fab digital twins are built with Omniverse and OpenUSD for process optimization.

In practice

• Co-develop memory for AI supercomputers.
• Use AI for semiconductor simulation.
• Implement digital twins for fab automation.

Topics

• AI Infrastructure
• Next-Gen Memory
• Semiconductor Manufacturing
• Digital Twins
• AI Factories
• NVIDIA CUDA-X

Best for: CTO, VP of Engineering/Data, Investor, AI Hardware Engineer, AI Architect, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI Magazine.