NVIDIA, SK hynix Team Up on Memory Tech for AI Factories

NVIDIA and SK hynix have signed a multiyear technology partnership on June 7, 2026 to develop next‑generation memory for the global AI factory buildout.

Context: AI factories need faster, cheaper memory

AI factories—large‑scale data centers dedicated to training and serving generative models—are expanding worldwide. Their performance hinges on high‑bandwidth, low‑latency memory, which currently accounts for a sizable share of capital and energy expenses. In a separate announcement, NVIDIA disclosed a gigawatt‑scale AI cloud in Korea that will rely on its DSX™ platform, with the first factory slated for 2027 (NVIDIA Newsroom). At the same time, NVIDIA and Doosan Group are deepening work on physical AI and factory infrastructure, blending accelerated computing with industrial automation (NVIDIA Newsroom). Across the industry, memory is being re‑thought: OpenAI recently unveiled a “Dreaming” memory system for ChatGPT to keep user preferences fresh across sessions (OpenAI Blog). The NVIDIA‑SK hynix deal fits this broader push to make memory more capable and cost‑effective.

Impact on AI infrastructure and costs

By co‑developing memory that can handle the massive data streams of AI models, the partners aim to shrink the power draw and silicon area required for each training job. Faster memory reduces the number of compute cycles needed, which directly cuts electricity bills and shortens time‑to‑market for new chips. For AI‑heavy enterprises, lower memory cost translates into a smaller total cost of ownership for AI factories, a factor that has traditionally inflated budgets.

SK hynix brings deep expertise in NAND and DRAM technologies, while NVIDIA contributes its full‑stack accelerated computing platforms. Their joint roadmap is expected to produce memory modules that align tightly with NVIDIA’s GPU and inference architectures, eliminating bottlenecks that force operators to over‑provision hardware. The expected outcome is a more efficient AI infrastructure stack where the memory‑to‑compute ratio is optimized for large language model workloads.

What comes next

The partnership is described as “multiyear,” indicating a roadmap that will span several product generations. Early milestones will likely focus on prototype validation in NVIDIA‑partnered AI factories, such as the upcoming Korean gigawatt‑scale cloud. As prototypes mature, the collaborators plan to scale manufacturing, leveraging SK hynix’s fab capacity to meet the projected demand from AI‑centric data centers.

Industry watchers will watch for the first memory‑enhanced AI server shipments, which could arrive as early as 2027, coinciding with the launch of the SK Telecom AI factory. If the memory advances deliver the projected efficiency gains, the cost per training flop could drop noticeably, making AI projects viable for a broader range of companies.

For now, the alliance signals a concerted effort to address one of the most stubborn cost drivers in AI infrastructure—memory. As the AI factory ecosystem matures, tighter integration between compute and memory may become the new baseline for building affordable, high‑performance AI services.