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▲ A scene from SK hynix Ventures Day, held in Silicon Valley in September

Identifying promising technologies early and investing in them is one challenge. Turning those technologies into businesses is another. That requires assessing what problems a technology can solve, refining it with research and development teams, and evaluating its potential for deployment

As AI workloads evolve toward inference and agentic AI, the criteria for system performance are also changing. Computing power alone is no longer enough. Where data is stored, how quickly it moves, and how efficiently it is processed now have

▪ Regarding Solidigm, no matters have been determined as of the date hereof. ▪ Whether utilizing external or internal capital, any approach must be evaluated not only by its structure, but also by its overall economic impact on existing shareholders.

AI is no longer defined by a single model or AI processor. For AI to operate effectively in real-world services and industrial applications, it takes faster compute, wider memory bandwidth, higher-performance networks, more efficient storage, and more stable power and

At the TSMC Open Innovation Platform(OIP) Conference, held on the 23rd(local time) in Santa Clara, California, US, SK hynix shared its next-generation memory portfolio and development strategy, and reaffirmed its solid partnership with TSMC by receiving the Partner of the

▲ Daesik Park, a distinguished engineer in PCIe Design, delivers a presentation

▲ SK hynix’s next-generation server DRAM lineup featured in this exhibition

▲ NVIDIA’s superchip “Vera Rubin,” equipped with SK hynix’s 12-layer 36GB HBM4 and 96GB SOCAMM2

▲ The SK hynix booth design, carrying a message that emphasizes the importance of memory in the AI era