THE APEX TIMES
NVIDIA and SK hynix strike multiyear tech pact aimed at boosting memory supply and speed for AI factories
The companies said the agreement focuses on next-generation memory development plus semiconductor design and manufacturing acceleration, using NVIDIA software tools for simulation and “fab digital twins.”
NVIDIA (NASDAQ: NVDA) and SK hynix announced a multiyear technology partnership on Sunday, positioning the collaboration around the memory needed to scale “AI factories” and to speed up semiconductor design and manufacturing.
In a joint statement distributed via Globe Newswire, the companies said the deal builds on years of co-engineering and is intended to advance next-generation memory while supporting the global buildout of AI infrastructure. NVIDIA CEO Jensen Huang said in the announcement that advanced memory is essential to AI factories’ performance, and credited SK hynix as a key partner for delivering memory technologies used in NVIDIA’s AI computing platforms.
The companies also tied the agreement to supply continuity, saying the partnership is designed to address the long development cycles of advanced memory and the capital-intensive steps involved in bringing new memory to production. The release did not provide specific capacity targets, pricing, or a timeline for any particular memory generation.
SK hynix, the statement said, will use NVIDIA’s CUDA-X libraries and AI methods to accelerate semiconductor simulation workflows, including technology computer-aided design (TCAD) and computational lithography workflows. It added that SK hynix will use NVIDIA PhysicsNeMo, described in the announcement as a framework for AI physics workflows, to speed core simulation workloads and “in-house simulation codes.”
The partnership extends beyond design software into manufacturing planning. SK hynix said it is developing “fab digital twins,” essentially 3D, software-based replicas of semiconductor factories and processes that can be used to visualize, simulate, and optimize operations. The companies said teams will build factory scenes using NVIDIA Omniverse libraries and OpenUSD pipelines, then apply NVIDIA cuOpt, described as a decision optimization engine, and NVIDIA Metropolis to support operational optimization such as coordinating autonomous mobile robots and other factory assets.
In addition, the announcement said the two companies are exploring ways to connect these digital twins with legacy software and “agentic AI” workflows, with the goal of enabling AI systems to reason over manufacturing data, automate tasks, and improve decision-making in the factory.
NVIDIA and SK hynix also said the agreement is intended to support the company’s broader product and infrastructure roadmap, with SK hynix expanding into markets NVIDIA is “creating” across AI infrastructure, personal AI, and physical AI. The release specifically cited NVIDIA Vera Rubin AI supercomputers, Vera CPUs, RTX Spark-powered PCs, and Jetson Thor robotic computing platforms, but did not specify whether the partnership targets any one memory type or product line.
The companies did not disclose financial terms, investment amounts, the exact memory technologies involved, or measurable performance objectives. Nor did they provide a public delivery schedule for future memory revisions, focusing instead on co-development, tooling, and supply continuity language.
If the partnership’s software and digital-twin efforts translate into faster ramp times for memory and improved manufacturing predictability, it could become an additional lever for NVIDIA’s broader AI factory strategy, where memory bottlenecks can constrain system throughput and timelines. Investors and customers will likely watch for later disclosures that connect the collaboration to specific memory generations, qualification results, and measurable supply improvements.
Why It Matters
- AI factory scaling depends not just on GPUs and networking, but also on advanced memory, which can require lengthy R&D and production ramp timelines.
- By coupling memory development with semiconductor design and manufacturing acceleration, NVIDIA is extending its “full-stack” approach beyond chips into the software and workflow layers used to create supply.
- Digital-twin efforts and optimization tooling could reduce iteration time for complex manufacturing decisions, potentially improving yield and predictability as new memory generations roll out.
- The announcement also indicates continued strategic alignment between NVIDIA and a major memory supplier, at a time when system-level performance can be constrained by memory availability and latency.
Sources
Key Facts
- NVIDIA and SK hynix announced a multiyear technology partnership aimed at advancing next-generation memory for AI factory buildouts.
- The companies said the agreement supports memory supply by addressing long development cycles and capital-intensive steps needed for advanced memory.
- SK hynix said it will use NVIDIA CUDA-X libraries and AI to accelerate semiconductor simulation workflows, including TCAD and computational lithography.
- SK hynix said it is using NVIDIA PhysicsNeMo for acceleration of in-house simulation codes and AI physics workloads.
- The partners said SK hynix is building fab digital twins using NVIDIA Omniverse libraries and OpenUSD pipelines for 3D factory scene visualization, simulation, and optimization.
- The release said operational optimization will draw on NVIDIA cuOpt and NVIDIA Metropolis, including coordination of autonomous mobile robots and other fab assets.
- The announcement did not disclose financial terms, capacity figures, or specific memory generation timelines.
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