{"id":10851,"date":"2026-07-08T03:12:03","date_gmt":"2026-07-08T03:12:03","guid":{"rendered":"https:\/\/news.skhynix.com\/?p=10851"},"modified":"2026-07-16T05:23:07","modified_gmt":"2026-07-16T05:23:07","slug":"hbm-to-essd","status":"publish","type":"post","link":"https:\/\/news.skhynix.com\/en\/hbm-to-essd\/","title":{"rendered":"From HBM to eSSD: How SK hynix is charting the future of full-stack memory"},"content":{"rendered":"

The focus of AI infrastructure competition is shifting. Larger models and faster compute engines remain important, but for AI systems to scale reliably across real-world services and industrial environments, it is becoming equally important to determine where data resides, which memory layer processes it, and how efficiently it can be retrieved when needed.<\/p>\n

This shift is also changing the role of memory companies. In the past, their primary role was to reliably supply products that customers\u00a0required. In the AI era, however, their role as technology partners is expanding to include a deeper understanding of customers\u2019 AI chips, system architectures, workloads, power\u00a0requirements,\u00a0and thermal designs.<\/p>\n

SK\u00a0hynix\u2019s\u00a0vision of becoming a full-stack AI memory creator reflects this transition. Building on its leadership in HBM, the company is extending its AI memory capabilities into AI-DRAM and AI-NAND while connecting product development with customer co-design and production and packaging infrastructure. Through this approach, SK\u00a0hynix\u00a0is strengthening its product portfolio, customer\u00a0collaboration,\u00a0and investment foundation to further advance its full-stack AI memory capabilities.<\/p>\n

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HBM leadership: The starting point of SK hynix\u2019s AI memory portfolio<\/h3>\n
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SK hynix\u2019s AI memory strategy begins with its leadership in HBM. Positioned close to AI accelerators, HBM delivers ultra-high-bandwidth data to support large-scale training and high-performance inference. As AI models grow and the amount of data to be processed increases, the ability to provide the data required by computing engines at the right time becomes a key factor in overall system efficiency.<\/p>\n

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Based on the technological capabilities it has built in the HBM market, SK hynix has continued to strengthen its position in AI memory. The company announced both the completion of HBM4 development and preparations for its mass production and also showcased its AI memory portfolio centered on HBM3E and HBM4 at major global technology events. HBM4 is a next-generation product designed to address the higher bandwidth and power efficiency requirements of high-performance AI systems. [Related article]<\/p>\n

The role of the logic die*<\/span> is becoming particularly important in HBM4. HBM4 is being developed in a direction that enhances base die performance, improving connectivity between the HBM stack and logic chips while reducing power consumption. Through its collaboration with TSMC, SK hynix is applying advanced logic process technology to the base die*<\/span> starting with HBM4, expanding the basis of HBM competitiveness beyond bandwidth to include logic dies, packaging, power efficiency, and customer system optimization.<\/p>\n

* <\/span>Logic die: A term emphasizing the base die\u2019s functional role, including control logic and input\/output.
\n* <\/span>Base die: A semiconductor die located at the bottom of an HBM stack that handles data input\/output and control functions between multiple DRAM dies and external logic chips. Starting with HBM4, the performance and process technology of the base die are becoming increasingly important factors for bandwidth, power efficiency, and customer system optimization.<\/div>\n

HBM technology is also expanding beyond bandwidth and capacity improvements to address stability and thermal management in high-density, high-power environments. SK\u00a0hynix\u2019s\u00a0iHBM*<\/sup><\/span>\u00a0solution is a thermal management technology concept aimed at next-generation HBM products, with a focus on improving operational stability and efficiency in high-density, high-bandwidth environments. This shows that the evolution of HBM now extends beyond product performance to include packaging,\u00a0power,\u00a0and thermal management at the system level.\u00a0[Related article<\/a>]<\/p>\n

* <\/span>iHBM: SK hynix\u2019s thermal management solution concept for next-generation HBM products.<\/div>\n
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Recently, HBM has been evolving beyond performance advancement to address customer system optimization. In its product roadmap beyond HBM4, SK hynix has identified HBM4E*<\/span> and custom HBM*<\/span> as key directions. Custom HBM is an approach that optimizes the base die and system structure of HBM according to customers\u2019 AI chip and workload requirements. As the needs of the AI market shift beyond general performance improvements toward inference efficiency and total cost of ownership (TCO)*<\/span> optimization, HBM is also evolving into a customized solution tailored to customer systems.<\/p>\n

* <\/span>HBM4E: An extended product generation following HBM4, targeting the bandwidth, capacity, and power efficiency required by next-generation AI systems.<\/div>\n
* <\/span>Custom HBM: Customized HBM that optimizes the base die, packaging structure, and other elements according to customers\u2019 AI chip architectures, workloads, and power and thermal design requirements.
\n* <\/span>Total cost of ownership (TCO): A concept that includes not only the cost of adopting a product, but also costs incurred throughout its lifecycle, such as power, operation, and maintenance.<\/div>\n

As ever, HBM remains the foundational cornerstone of SK hynix\u2019s AI memory portfolio. However, as AI infrastructure becomes more advanced, HBM needs to be understood not as a standalone product, but as one layer within a broader memory hierarchy that connects system memory with NAND-based storage.<\/p>\n

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AI-DRAM: Extending HBM leadership to system memory<\/h3>\n
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AI infrastructure does not\u00a0operate\u00a0on accelerators alone. In AI servers, data centers, AI PCs, and on-device AI environments, CPUs, AI accelerators, networks,\u00a0storage,\u00a0and system memory must work\u00a0together within a singular\u00a0architecture. In this structure, AI-DRAM supports performance across servers and overall systems.<\/p>\n

SK hynix is extending the technological capabilities that have built HBM into AI-DRAM. SOCAMM2,*<\/span> GDDR7, DDR5, and LPDDR are part of a product lineup that supports the system memory performance and power efficiency\u00a0required\u00a0for AI servers, high-performance computing, data\u00a0centers,\u00a0and on-device AI environments.<\/p>\n

* <\/span>Small Outline Compression Attached Memory Module (SOCAMM2): An AI server-optimized memory module based on low-power DRAM. SK hynix previously announced the mass production of 192GB SOCAMM2 using 1cnm LPDDR5X.<\/div>\n

The development direction of AI-DRAM goes beyond improving the performance of conventional DRAM. It is becoming more specialized across low-power, high-performance products that improve data center operating efficiency and reduce TCO, high-bandwidth products optimized for AI servers and accelerated systems, and low-power mobile memory suited for on-device AI. SK hynix\u2019s segmentation of its product lineup under the AI-DRAM concept reflects this trend.<\/p>\n

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SOCAMM2 is one of the representative products designed to improve memory performance in AI servers. Responding to demand for higher-performance memory in AI servers, SK\u00a0hynix\u00a0has begun mass production of 192GB SOCAMM2,\u00a0utilizing 1cnm LPDDR5X low-power DRAM. Designed to improve the power and space efficiency of server memory modules, SOCAMM2 illustrates how AI server memory configurations are changing. [Related article<\/a>]<\/p>\n

DDR5 also plays\u00a0an important role\u00a0in system memory. Through its server DDR5 products, SK\u00a0hynix\u00a0supports the speed, power efficiency, and system stability required by AI servers and data center environments. As AI workloads expand beyond servers and data centers into a wider range of computing environments, the role of system memory alongside HBM continues to grow.<\/p>\n

GDDR7\u00a0sees\u00a0broader use in high-performance graphics and compute environments. In areas that require large-scale data processing, such as AI, high-performance computing, graphics, and autonomous driving, memory with high speed and power efficiency is essential. LPDDR also plays\u00a0an important role\u00a0in on-device AI,\u00a0mobile,\u00a0and edge environments. As AI functions expand from the cloud to personal devices and connected devices, low-power DRAM that can deliver performance within limited power and space constraints\u00a0becomes\u00a0increasingly important. As AI workloads expand beyond servers and data centers into a broader range of applications, SK hynix continues to advance next-generation ultra-high-capacity, high-performance memory technologies, including PIM and CXL. These innovations provide the foundation for meeting growing demand for high-performance DRAM in emerging AI applications such as robotics, mobility, and industrial automation.<\/p>\n

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AI-NAND: A storage axis for data-centric AI<\/h3>\n
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As AI expands from training to inference and services, the role of NAND is also growing. AI services continuously generate and utilize user requests, contextual data, search data, model-related data, and application data. In services that require agentic AI and long-context processing in particular, the process of storing, retrieving, and reusing data affects system performance.<\/p>\n

SK hynix\u2019s AI-NAND strategy responds to this data-centric AI environment. As the AI inference market grows, demand is also increasing for NAND that can process large volumes of data quickly and efficiently. AI-NAND provides a foundation for storing large-scale data reliably across a broader data layer and accessing it quickly when needed. SK hynix presents the direction of AI-NAND across three dimensions: performance, bandwidth, and density.
\nFirst, AI-N Performance (AIN-P)*<\/span> delivers higher input\/output performance with high-performance SSDs designed around small chunk sizes.*<\/span>
\nThis supports the fast data access required for inference services and data-centric workloads. AI-N Bandwidth (AIN-B)8 focuses on strengthening high-bandwidth characteristics so that NAND can be used together with HBM, with High Bandwidth Flash (HBF)*<\/span> as a representative example. AI-N Density (AIN-D)*<\/span> focuses on improving capacity and cost efficiency based on QLC11 NAND and SSDs, with an emphasis on securing storage density and price competitiveness that can compete with HDDs in data center environments.<\/p>\n

* <\/span>AIN-P, AIN-B, AIN-D: SK hynix\u2019s direction for AI-NAND. AIN-P focuses on performance, AIN-B on bandwidth, and AIN-D on density and cost efficiency.<\/div>\n
* <\/span>Chunk size: The amount of data that a storage system processes at once when reading or writing data. Smaller chunk sizes are advantageous for AI workloads that require more granular data access as they can help improve input\/output efficiency.
\n* <\/span>High Bandwidth Flash (HBF): A next-generation NAND-based technology, considered a new memory layer between HBM and SSDs.<\/div>\n
* <\/span>Quad-level cell (QLC): A technology that stores four bits of data in a single NAND cell, making it advantageous for implementing high-capacity storage.<\/div>\n

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Among these, the 245TB high-capacity QLC-based storage solution and next-generation high-performance storage technologies serve as concrete examples of SK\u00a0hynix\u2019s\u00a0AI-NAND strategy for addressing the storage and access requirements of data center and enterprise AI environments. QLC can store more data in a single cell, making it suitable for high-capacity storage, while SK\u00a0hynix\u2019s\u00a0mass production of 321-layer QLC NAND is linked to rising demand for large-scale data in AI data centers and enterprise environments.<\/p>\n

HBF is also drawing attention as a next-generation storage technology for the AI inference era. Positioned as a new memory layer between HBM and SSDs, HBF is being discussed as a way to improve the efficiency of data processing for large-scale computation data and KV caches*<\/span> generated during AI inference. SK\u00a0hynix\u00a0is also\u00a0participating\u00a0in efforts to build the HBF ecosystem through global standardization collaboration. [Related article<\/a>]<\/p>\n

* <\/span>KV cache: A technology that stores and reuses previously generated key and value vectors to reduce the inefficiency of repeating earlier calculations.<\/div>\n

AI-NAND is evolving beyond simple storage to improve data accessibility and scalability in AI infrastructure. While HBM provides ultra-high bandwidth close to compute and AI-DRAM supports the system memory domain, AI-NAND handles large-scale data storage and utilization, forming a key pillar of the full-stack AI memory portfolio.<\/p>\n

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AI memory solutions designed together with customers<\/h3>\n
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SK hynix\u2019s strategy is not limited to expanding its product portfolio. AI companies and cloud service providers require memory solutions tailored to their AI chips, system architectures, and service objectives. As a result, the role of memory companies is expanding beyond supplying finished products to become technology partners that understand customer system requirements and engage in discussions from the initial design stage.<\/p>\n

Co-design involves more than product specifications. To realize the performance and efficiency required in actual system environments, AI chip architectures, memory bandwidth, capacity, packaging methods, power efficiency, thermal management, and data movement paths must be reviewed in advance together with customers. High-performance memory such as HBM is closely linked with AI chips, packaging structures, power design, and thermal management.<\/p>\n

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This type of collaboration model is also aligned with the\u00a0vision of becoming a full-stack AI memory creator presented at SK AI Summit 2025. At the event, SK\u00a0hynix\u00a0shared its direction to expand its role from merely a \u201cprovider\u201d that supplies the products customers need at the right time to a \u201ccreator\u201d that works with customers and partners to solve challenges.<\/p>\n

SK\u00a0hynix\u00a0is also strengthening its organizational structure and research foundation to respond more closely to changes in the AI ecosystem. Its plan to\u00a0establish\u00a0a U.S.-based AI company is a step toward expanding opportunities for AI system-level optimization and collaboration within the data center ecosystem. Based on its global research capabilities, the company is also deepening its understanding of computing systems and exploring next-generation memory solutions suited to customer workloads and system architectures. [Related\u00a0article<\/a>]<\/p>\n

The strengthening of this organizational and research foundation shows that the role of memory companies is expanding beyond product supply to defining and solving customer challenges together. While each product lineup serves as an important technological foundation, collaboration from the initial design stage is also necessary to realize performance and efficiency within customer systems.<\/p>\n

SK hynix\u2019s recently announced multi-year technology partnership with NVIDIA also reflects this shift. The two companies plan to jointly develop next-generation memory for AI factories and expand memory development collaboration aligned with NVIDIA\u2019s AI infrastructure roadmap. Competition for AI memory is moving beyond product supply toward a model that considers customer system roadmaps as well as development and manufacturing\u00a0environments. [Related article<\/a>]<\/p>\n

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Production and packaging infrastructure: The foundation for execution<\/h3>\n
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However, full-stack AI memory creator capabilities cannot be completed through product roadmaps and customer collaboration alone. In an environment where demand for AI memory is growing rapidly, manufacturing and packaging infrastructure capable of reliably producing and supplying high-performance products is also critical.<\/p>\n

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To meet growing demand for high-performance AI memory, including HBM, SK hynix is expanding its production and packaging infrastructure. Investments in the\u00a0Yongin\u00a0Semiconductor Cluster, Cheongju NAND FAB, and southwestern Korea can be viewed as key foundations for strengthening SK hynix\u2019s manufacturing base in Korea. Together with its advanced packaging facility in Indiana, U.S., SK hynix is further expanding the foundation needed to strengthen its AI memory production capabilities and supply stability.<\/p>\n

The\u00a0Yongin\u00a0Semiconductor Cluster is a core site for securing a long-term memory production foundation. Cheongju M15X is being developed as a production hub\u00a0optimized\u00a0for next-generation DRAM, such as HBM, while Cheongju P&T7 is being\u00a0established\u00a0as an advanced packaging\u00a0fab\u00a0dedicated to AI memory products, including HBM. The southwestern production belt expands SK hynix\u2019s domestic manufacturing base by connecting Yongin and Cheongju, reinforcing the company\u2019s long-term strategy to meet growing global demand for high-performance memory as AI adoption continues to expand. The advanced packaging facility in Indiana, U.S. will strengthen packaging and R&D capabilities for AI products and accelerate collaboration with customers in North America. [Related article<\/a>]<\/p>\n

AI memory is an area where high-performance DRAM and NAND, advanced packaging, and customized solutions are\u00a0closely connected. HBM and next-generation high-performance memory require packaging technology and production stability, while high-capacity storage products require a stable supply foundation aligned with data center\u00a0demand.<\/p>\n

SK\u00a0hynix\u2019s\u00a0simultaneous investment in production infrastructure and packaging is not simply an expansion of facilities. It is a foundation upon which to reliably supply its AI memory portfolio in line with market demand and respond to the pace of customers\u2019 AI system development. At the same time, it is also a process of strengthening fundamental competitiveness by advancing process scaling, advanced packaging,\u00a0automation,\u00a0and smart factories, as well as quality and reliability.<\/p>\n

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Charting the path toward becoming a full-stack AI memory creator<\/h3>\n
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SK hynix\u2019s AI memory strategy is built on four key pillars. The first is its leadership in HBM technologies that addresses the growing demands of next-generation AI infrastructure.<\/p>\n

The second is an AI-DRAM portfolio that extends across SOCAMM2, GDDR7, and DDR5. The third is an AI-NAND portfolio that includes 245TB QLC-based high-capacity storage and next-generation high-performance storage technologies. The fourth is an execution framework that connects product competitiveness to real-world AI systems through customer co-design, manufacturing capabilities, and advanced packaging infrastructure.<\/p>\n

Together, these four pillars turn the vision of becoming a full-stack AI memory creator into a clear execution strategy. Through a portfolio spanning HBM, AI-DRAM, and AI-NAND, collaboration across the global AI ecosystem, and continued investment in manufacturing and advanced packaging, SK hynix is building the foundation for next-generation AI systems.<\/p>\n

As AI infrastructure expands across industries and real-world services, customers require AI memory solutions \u2014 not just individual memory products \u2014 that are designed around specific system architectures and workloads while ensuring reliable, scalable supply. Building on its full-stack AI memory capabilities, SK hynix will continue working alongside customers to build AI systems that are more efficient, scalable, and optimized for production environments.<\/p>\n","protected":false},"excerpt":{"rendered":"

The focus of AI infrastructure competition is shifting. Larger models and faster compute engines remain important, but for AI systems to scale reliably across real-world services and industrial environments, it is becoming equally important to determine where data resides, which<\/p>\n","protected":false},"author":23,"featured_media":10857,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"_migrated_source_id":0,"footnotes":"","_members_access_role":[],"_members_access_error":""},"categories":[5],"tags":[12,14,1584,51,25,13,19],"class_list":["post-10851","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-tech-and-ai","tag-ai","tag-ai-memory","tag-chbm","tag-dram","tag-essd","tag-hbm","tag-nand"],"acf":[],"_links":{"self":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts\/10851","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/users\/23"}],"replies":[{"embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/comments?post=10851"}],"version-history":[{"count":2,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts\/10851\/revisions"}],"predecessor-version":[{"id":10973,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/posts\/10851\/revisions\/10973"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/media\/10857"}],"wp:attachment":[{"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/media?parent=10851"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/categories?post=10851"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/news.skhynix.com\/en\/wp-json\/wp\/v2\/tags?post=10851"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}