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\n Collaboration Supports Next-Generation Memory Codevelopment With NVIDIA\u2019s AI Infrastructure Roadmap and Expands Supply for the Accelerating Global AI Factory Buildout<\/p>\n
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\n Collaboration Supports Next-Generation Memory Codevelopment With NVIDIA\u2019s AI Infrastructure Roadmap and Expands Supply for the Accelerating Global AI Factory Buildout<\/p>\n
NVIDIA and SK hynix announced a multiyear technology partnership to advance next-generation memory for the global AI factory buildout and accelerate semiconductor design and manufacturing. The agreement builds on years of deep co-engineering collaboration that has powered some of the world\u2019s most advanced AI computing platforms.<\/p>\n
\u201cAI factories are the engines of the next industrial revolution, and advanced memory is essential to their performance,\u201d said Jensen Huang, founder and CEO of NVIDIA. \u201cSK hynix has been an extraordinary partner to NVIDIA, playing a central role in delivering advanced memory technologies for NVIDIA AI computing platforms. Together, we will codevelop the next generation of memory for AI factories and support the accelerating global expansion of AI infrastructure \u2014 from frontier model training to agentic and physical AI.\u201d<\/p>\n
Also Read:\u00a0AiThority Interview with Matej Bukovinski, Chief Technology Officer at Nutrient<\/a><\/p>\n \u201cSK hynix and NVIDIA have been building toward this for years, and this partnership reflects the depth of that collaboration,\u201d said Chey Tae-won, Chairman of SK Group. \u201cTogether, we are codeveloping the next generation of memory for AI factories and applying AI to how we design and manufacture semiconductors \u2014 work that will shape the future of AI infrastructure.\u201d<\/p>\n The multiyear agreement supports supply to address the extended development cycles of advanced memory. As AI factories scale globally, this strategic partnership enables memory supply to keep pace with NVIDIA\u2019s infrastructure roadmap and the sustained buildout of AI infrastructure worldwide. Through this partnership, SK hynix will diversify into new markets NVIDIA is creating \u2014 spanning AI infrastructure, personal AI and\u00a0physical AI\u00a0\u2014 codeveloping memory for\u00a0NVIDIA Vera Rubin AI supercomputers,\u00a0NVIDIA Vera CPUs,\u00a0NVIDIA RTX Spark\u2122-powered PCs\u00a0and\u00a0NVIDIA Jetson Thor\u2122 robotic computing platforms.<\/p>\n Accelerating Technology Computer-Aided Design and Semiconductor Simulation SK hynix is also using CUDA-X and the\u00a0NVIDIA PhysicsNeMo\u2122 framework to deliver core-workload acceleration across its in-house simulation codes and AI physics workflows.<\/p>\n By extending these tools to the semiconductor electronic design automation and simulation ecosystems, this initiative paves the way for three-way collaborations among chipmakers, NVIDIA and electronic design automation software vendors.<\/p>\n Advancing Fab Digital Twins for Autonomous Manufacturing These digital twins can also support operational optimization, including the movement of autonomous mobile robots and other fab assets, using the open source, GPU-accelerated\u00a0NVIDIA cuOpt\u2122 decision optimization engine and the\u00a0NVIDIA Metropolis\u00a0platform.<\/p>\n The companies are also exploring ways to connect digital twins with existing legacy software and agentic AI workflows, enabling AI systems to reason over fab data, automate tasks and improve manufacturing decision-making.<\/p>\n Also Read:\u00a0\u200b\u200b<\/a>AI systems \u2013 Interoperable AI systems: Connecting models across platforms<\/a><\/p>\n
SK hynix is using\u00a0NVIDIA CUDA-X\u2122 libraries and AI to speed semiconductor simulation, including technology computer-aided design and computational lithography workflows.<\/p>\n
SK hynix is developing fab\u00a0digital twins\u00a0as a foundation for autonomous fab operations. Teams can use scene optimization technologies, as well as\u00a0NVIDIA Omniverse\u2122\u00a0libraries and\u00a0OpenUSD\u00a0pipelines, to build 3D factory scenes for visualizing, simulating and optimizing complex semiconductor manufacturing environments.<\/p>\n