Nvidia Forms AI Infrastructure Partnerships in South Korea
Reporting by SiliconANGLE and Reuters shows that Nvidia announced partnerships with South Korea's SK Hynix, Naver and Doosan to expand AI data center infrastructure. Reuters reports that the agreements include a multiyear technology collaboration with SK Hynix to develop next-generation memory for large AI data centers and that SK Telecom, SK Hynix's sister company, will build a gigawatt-scale AI cloud with the first data center expected online in 2027. SiliconANGLE quotes CEO Jensen Huang saying, "Advanced memory is at the core of their performance," and describes technical cooperation on Nvidia's CUDA-X and PhysicsNeMo toolchains. Reporting by Chosun and KED adds that Naver and Nvidia are pursuing a GW-scale AI factory roadmap beginning with a 55MW project in 2027.
What happened
Reporting by SiliconANGLE and Reuters shows that Nvidia announced a slate of partnerships with South Korean technology groups, including SK Hynix, Naver and Doosan, during CEO Jensen Huang's visit to Seoul in early June 2026. Reuters reports that Nvidia and its partners did not disclose deal values, and that SK Hynix and Nvidia signed a multiyear technology partnership to advance next-generation memory for global AI data centers. Reuters reports that SK Telecom will build a gigawatt-scale AI cloud using Nvidia technology, with the first AI data center expected to come online in 2027. SiliconANGLE quotes Jensen Huang: "Advanced memory is at the core of their performance." Chosun reports that Naver and Nvidia agreed on a GW-scale AI factory roadmap starting with a 55MW build in 2027.
Technical details
Reporting by SiliconANGLE notes that the SK Hynix collaboration includes use of Nvidia's CUDA-X library and its PhysicsNeMo framework to accelerate semiconductor simulation workflows, including technology computer-aided design and computational lithography. KED reports that SK Hynix plans to ship final samples of HBM4 to Nvidia, and that South Korea's memory suppliers and fabs are scaling production to meet next-generation HBM demand. Chosun and MarketScreener describe Nvidia's DSX infrastructure platform as a focal point for integration with hyperscale data centers run by Naver.
Industry context
Editorial analysis - technical context: Companies building large-scale AI deployments increasingly link GPU vendors directly with memory suppliers and data center operators to shorten supply-chain cycles for specialized components like HBM4 and to co-design systems for power and thermal efficiency. Observers note that simulation toolchains (CUDA-X, PhysicsNeMo) and close vendor-supplier engineering are commonly used to reduce iteration time between architecture changes and manufacturing validation in advanced-node chip programs.
Context and significance
The reported agreements combine three elements that matter to practitioners: upstream memory supply (SK Hynix and HBM4 sampling reported by KED), hyperscale data center capacity and operations (Naver's reported GW roadmap and SK Telecom's gigawatt-scale cloud), and integration of software/hardware stacks (Nvidia DSX, CUDA-X, PhysicsNeMo). For ML engineers and infrastructure teams, stronger regional ecosystems that bundle chips, memory, and data center capacity can lower procurement friction for large training runs and reduce lead-times for next-generation accelerators.
What to watch
For practitioners: monitor:
- •formal announcements or technical briefs from Nvidia, SK Hynix, and Naver about the specifications and availability timelines for HBM4 and Vera Rubin-class accelerators
- •public timelines for Naver's 55MW initial build and the SK Telecom gigawatt cloud, which Reuters and Chosun cite as targeting 2027
- •any disclosed integrations or benchmarks showing DSX performance at scale. Also watch energy and cooling designs reported by Doosan and partners, since reports mention Doosan's involvement in energy solutions for data center platforms
Scoring Rationale
The story concerns infrastructure critical to large-scale model training and deployment: partnerships that tie GPU vendors to memory suppliers and hyperscalers materially affect capacity, procurement timelines, and system co-design. That makes it notable for ML infrastructure teams and procurement leads.
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