SK Hynix stock price hits a new high: delivers HBM4E samples, with its leading position in AI memory validated again

Original Title: "Stock Price Hits New High! SK Hynix Starts Shipping 12-Layer HBM4E Samples"
Original Author: Zhao Ying, Wall Street Insights

SK Hynix delivers HBM4E samples to major clients. This flagship 12-layer stacked memory features a per-pin speed of 16Gbps, over 20% improvement in power efficiency, a 17% reduction in heat dissipation resistance, and a single chip capacity of 48GB. Once the news broke, the company's stock surged intraday by 7.3% to a record high, and market expectations for its continued leadership in the AI memory race heated up.

SK Hynix announced the delivery of next-generation AI memory chip HBM4E samples to major clients, pushing the company's stock to a new all-time high.

On Thursday, SK Hynix stated on its official website that this 12-layer stacked HBM4E product achieves a maximum data processing speed of 16Gbps per pin, with over 20% improvement in power efficiency compared to the previous generation, and reduces heat dissipation resistance by 17% through advanced packaging technology. SK Hynix said it will work closely with partners to ensure timely mass production of the product.

This sample shipment marks a further acceleration in SK Hynix’s technological iteration in high-bandwidth memory, strengthening its core position in the AI infrastructure supply chain, and signaling to the market that the company continues to lead the HBM technology roadmap.

Following the announcement, SK Hynix’s stock rose 7.3% intraday on the Korean trading platform, reaching a new record high. This increase reflects strong market expectations for the company's continued leadership in the AI memory sector. From HBM3, HBM3E to HBM4, SK Hynix has established a complete delivery capability from mass production to supply. The timely shipment of HBM4E samples further reinforces investor confidence in its technological execution.

Performance and efficiency both leap forward

SK Hynix disclosed in its statement that the 12-layer HBM4E has achieved significant improvements in both performance and power efficiency.

Specifically, the product’s maximum data processing speed per pin reaches 16Gbps, with over 20% improvement in power efficiency over the previous generation. Meanwhile, HBM4E uses the latest interface design and optimization to effectively reduce data transfer latency and maintain stable operation in high-bandwidth environments. These features directly enhance data processing capabilities in AI training and inference scenarios, helping clients improve operational efficiency in AI data centers and large-scale computing systems.

Advanced packaging technology supports 48GB capacity

In terms of packaging process, SK Hynix employs Advanced MR-MUF (Large-Scale Reflow Molding Bottom Filling) technology to achieve a single-chip capacity of 48GB under the 12-layer stacking structure, while ensuring structural stability.

The MR-MUF process involves injecting liquid protective material between chips to safeguard circuits. SK Hynix further optimized this process, reducing the heat dissipation resistance of HBM4E by 17% compared to the previous HBM4 generation, ensuring stable operation of memory chips in high-performance computing environments. This technological breakthrough is especially critical for AI data centers operating under sustained high loads.

SK Hynix President and Chief Development Officer Ahn Hyun stated: "With market-leading technological capabilities and manufacturing expertise, SK Hynix has laid a solid foundation for strengthening its AI leadership through HBM4E. Through close collaboration with partners, we will deliver the value needed by the market and further solidify our position as a full-stack AI memory creator."

SK Hynix emphasized that its extensive experience in mass production and supply of HBM3, HBM3E, and HBM4 has been a key foundation for the timely delivery of this HBM4E sample. The company stated it will rely on market-validated product reliability and supply capacity to support the development of next-generation infrastructure and help address performance bottlenecks in AI systems.

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