SK Hynix Stock Price Hits New High: Delivers HBM4E Samples, Reinforcing Its Leading Position in AI Memory

marsbitPublicado a 2026-06-18Actualizado a 2026-06-18

Resumen

SK Hynix Delivers HBM4E Samples, Shares Hit Record High on Strong AI Memory Outlook SK Hynix has delivered samples of its next-generation AI memory chip, HBM4E, to major customers, sending its stock price soaring 7.3% to a historic high. The new 12-layer stacked flagship product offers a data processing speed of 16Gbps per pin, a more than 20% improvement in power efficiency, and a 17% reduction in thermal resistance compared to the previous generation. It also achieves a single-chip capacity of 48GB, enabled by Advanced MR-MUF packaging technology. This sample delivery accelerates SK Hynix's technological iteration in the high-bandwidth memory (HBM) field, solidifying its core position in the AI infrastructure supply chain. The market response reflects strong confidence in the company's ability to maintain leadership in the AI memory race, building on its established track record of mass production and supply for HBM3, HBM3E, and HBM4. The performance and efficiency gains of HBM4E are expected to enhance data processing capabilities in AI training and inference scenarios, addressing performance bottlenecks in next-generation AI systems.

SK Hynix has delivered HBM4E samples to key customers. This flagship 12-layer stacked memory achieves a data transfer speed of 16Gbps per pin, improves power efficiency by over 20%, reduces thermal resistance by 17%, and offers a single-chip capacity of 48GB. Upon the news, the company's stock price surged 7.3% intraday to a record high, fueling market expectations for its continued leadership in the AI memory race.

SK Hynix announced the delivery of samples of its next-generation AI memory chip, HBM4E, to key customers, pushing its stock price to a historic high.

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

This sample delivery marks SK Hynix's accelerated technological iteration in the high-bandwidth memory field, further solidifying its core position in the AI infrastructure supply chain and providing the market with the latest signal of the company's continued leadership in the HBM technology roadmap.

Following the announcement, SK Hynix's stock price rose 7.3% intraday on the Korean trading platform, reaching a record intraday high. This gain reflects the market's strong anticipation of the company's sustained lead in the AI memory race. From HBM3 and HBM3E to HBM4, SK Hynix has established complete delivery capabilities from mass production to supply. The on-time delivery of these HBM4E samples further strengthens investor confidence in its ability to deliver on its technological promises.

Dual Leap in Performance and Efficiency

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

Specifically, the product achieves a maximum data processing speed of 16Gbps per pin, with power efficiency improved by over 20% compared to the previous generation. Additionally, HBM4E effectively reduces data transmission latency through its latest interface design and optimization, while maintaining stable operation in high-bandwidth environments. These features directly enhance data processing capabilities in AI training and inference scenarios, helping customers improve operational efficiency in AI data centers and large-scale computing systems.

Advanced Packaging Technology Enables 48GB Capacity

In terms of packaging process, SK Hynix employs Advanced MR-MUF (Mass Reflow-Molded Underfill) technology to achieve a single-chip capacity of 48GB within a 12-layer stacked structure while ensuring structural stability.

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

In a statement, Ahn Hyun, President and Chief Development Officer of SK Hynix, said: "SK Hynix, with its market-leading technology capabilities and manufacturing expertise, is laying the groundwork to reinforce its AI leadership based on HBM4E. Through close collaboration with our partners, we will deliver the value the market needs, while further solidifying our technological leadership position as a full-stack AI memory creator."

SK Hynix emphasized that its accumulated experience in the mass production and supply of HBM3, HBM3E, and HBM4 served as an important foundation for the on-time delivery of these HBM4E samples. The company stated it will leverage its market-validated product reliability and supply capabilities to support the development of next-generation infrastructure and help address performance bottlenecks in AI systems.

Criptos en tendencia

Preguntas relacionadas

QWhat new AI memory product samples did SK Hynix deliver, and what was the market reaction?

ASK Hynix delivered samples of its next-generation HBM4E memory chips to key customers. Following the announcement, the company's stock price surged 7.3% during the trading session, reaching a new all-time high.

QWhat are the key performance improvements of SK Hynix's 12-layer HBM4E memory?

AThe 12-layer HBM4E features a per-pin data processing speed of 16Gbps, a power efficiency improvement of over 20% compared to the previous generation, and a 17% reduction in thermal resistance.

QHow does the Advanced MR-MUF packaging technology benefit the HBM4E chip?

AThe Advanced MR-MUF (Mass Reflow-Molded Underfill) technology enables a single HBM4E chip to achieve a capacity of 48GB within a 12-layer stacked structure. It also ensures structural stability and lowers thermal resistance by 17%, which is crucial for stable operation in high-performance AI data centers.

QWhat does SK Hynix claim about its position in the AI memory market based on this development?

ASK Hynix claims that the timely delivery of HBM4E samples reinforces its core position in the AI infrastructure supply chain and signals its continued leadership in HBM technology. The company positions itself as a 'full-stack AI memory creator' solidifying its technical leadership.

QWhat prior experience does SK Hynix cite as crucial for the HBM4E sample delivery?

ASK Hynix cites its extensive prior experience in the mass production and supply of HBM3, HBM3E, and HBM4 products as the critical foundation that enabled the on-schedule delivery of the HBM4E samples.

Lecturas Relacionadas

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

AI is reshaping the labor market's value proposition. The traditional four-year college degree is losing its appeal as a guaranteed career path, while skilled blue-collar trades like electricians, welders, and plumbers are experiencing historic demand and wage premiums. This shift is driven by dual pressures: AI's displacement of certain white-collar roles and a booming need for physical infrastructure and data center construction. Data confirms the trend. In the U.S., vocational school revenue surged, and a significant portion of recent layoffs are AI-related. Surveys show a majority of Gen Z adults plan to pursue blue-collar work, citing better job security against AI automation. Vocational education interest has exploded recently. Experts cite a psychological shift as younger generations seek tangible, AI-resistant careers and avoid high student debt. In many cases, salaries for skilled trades now match or exceed those requiring a bachelor's degree. In South Korea, semiconductor vocational high schools boast near-total employment, with graduates securing high-paying roles at companies like Samsung. The shortage is structural, exacerbated by a retiring baby boomer workforce and massive infrastructure projects. Companies like JPMorgan Chase, Meta, and Lowe's are investing heavily in training programs. However, overcoming historical stigma and a "perception gap" around trade careers remains a key challenge to closing the talent gap.

marsbitHace 58 min(s)

From South Korea to the United States: Blue-Collar Jobs Are Becoming Increasingly Popular, Thanks to AI

marsbitHace 58 min(s)

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

Qualcomm reported its Q3 FY2026 results (ending June 2026), with revenue of $9.95B, down 4% YoY but above expectations. Gross margin declined to 53.1%, pressured by rising costs across manufacturing and memory. Key business segments showed mixed performance: Handset revenue fell 19.6% YoY to $5.09B, dragged by an 11% decline in non-Apple Android shipments and weaker high-end mix. Conversely, Automotive revenue surged 61% to $1.59B, and IoT grew 9% to $1.83B. Core operating profit dropped 41% YoY due to margin compression and higher expenses. Management's Q4 FY2026 guidance projects revenue of $9.7B-$10.5B, in line with consensus, but Non-GAAP EPS guidance of $2.05-$2.25 fell short of expectations. Amidst persistent weakness in its core handset market, Qualcomm is pursuing growth in AI, focusing on Edge AI (smartphones, PCs, automotive) and Data Center AI. Its data center strategy includes four pillars: AI accelerators (e.g., AI200), commercial CPUs (Dragonfly C1000), custom silicon, and connectivity solutions. While these initiatives initially boosted its stock, concerns over AI capital expenditure sustainability have since erased those gains. The company targets $5B in data center revenue for FY2027 and $15B for FY2029. The report concludes that with the traditional handset business still under pressure, the data center opportunity is currently viewed as a longer-term option, and a more conservative valuation based on core operations may be warranted until AI contributions materialize.

marsbitHace 1 hora(s)

Qualcomm: AI Hype Subsides, When Will Smartphones Emerge from the Gloom?

marsbitHace 1 hora(s)

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

At the 2026 YC Startup School, Jeff Dean outlined his vision for AI's next phase, shifting focus from simply scaling models to building intelligent, autonomous systems. He believes AI's progress is no longer just about creating smarter models, but about integrating them into systems capable of long-term, iterative work, automated experimentation, and continuous learning. This evolution moves the competition from "who has the bigger model" to "who can best organize intelligence." Dean suggests AI capabilities are now comparable to a junior engineer, enabling the automation of complex workflows. However, the true challenge and opportunity lie in managing these AI "workers" at scale. He emphasizes the importance of **context engineering**—structuring tools, memory, and feedback loops—over raw model power. For startups, this means building deep expertise in niche domains where general models currently fail (near 0-1% success rates), leveraging proprietary data, specialized tools, and domain-specific evaluators. A recurring theme is re-examining fundamental constraints. Dean's past work, like moving Google's search index to memory or creating the TPU, stemmed from questioning outdated assumptions about hardware and cost. He sees similar inflection points today, particularly in **specialized inference hardware** to drastically reduce latency and energy consumption for real-time Agent operation. Notably, he points out that in modern AI systems, the dominant cost is often not computation but **data movement**. Reliable, long-running Agents require robust system design, borrowing concepts from distributed computing like checkpointing, state management, and parallel exploration to handle failures and maintain progress over days or weeks. As AI automates execution, the scarcest human skills will shift to **defining clear specifications**, **judging what problems are worth solving** (taste), and designing effective feedback loops. Ultimately, Dean's framework prioritizes understanding the problem deeply, identifying the true bottlenecks, and systematically building closed-loop systems where AI can not only perform tasks but also improve AI itself.

marsbitHace 1 hora(s)

From TPU to Self-Evolving Agents: How Jeff Dean Predicts the Next Step in AI

marsbitHace 1 hora(s)

Trading

Spot

Artículos destacados

Cómo comprar LAYER

¡Bienvenido a HTX.com! Hemos hecho que comprar Solayer (LAYER) sea simple y conveniente. Sigue nuestra guía paso a paso para iniciar tu viaje de criptos.Paso 1: crea tu cuenta HTXUtiliza tu correo electrónico o número de teléfono para registrarte y obtener una cuenta gratuita en HTX. Experimenta un proceso de registro sin complicaciones y desbloquea todas las funciones.Obtener mi cuentaPaso 2: ve a Comprar cripto y elige tu método de pagoTarjeta de crédito/débito: usa tu Visa o Mastercard para comprar Solayer (LAYER) al instante.Saldo: utiliza fondos del saldo de tu cuenta HTX para tradear sin problemas.Terceros: hemos agregado métodos de pago populares como Google Pay y Apple Pay para mejorar la comodidad.P2P: tradear directamente con otros usuarios en HTX.Over-the-Counter (OTC): ofrecemos servicios personalizados y tipos de cambio competitivos para los traders.Paso 3: guarda tu Solayer (LAYER)Después de comprar tu Solayer (LAYER), guárdalo en tu cuenta HTX. Alternativamente, puedes enviarlo a otro lugar mediante transferencia blockchain o utilizarlo para tradear otras criptomonedas.Paso 4: tradear Solayer (LAYER)Tradear fácilmente con Solayer (LAYER) en HTX's mercado spot. Simplemente accede a tu cuenta, selecciona tu par de trading, ejecuta tus trades y monitorea en tiempo real. Ofrecemos una experiencia fácil de usar tanto para principiantes como para traders experimentados.

385 Vistas totalesPublicado en 2025.02.11Actualizado en 2026.06.02

Cómo comprar LAYER

Discusiones

Bienvenido a la comunidad de HTX. Aquí puedes mantenerte informado sobre los últimos desarrollos de la plataforma y acceder a análisis profesionales del mercado. A continuación se presentan las opiniones de los usuarios sobre el precio de LAYER (LAYER).

活动图片