{"id":1002,"date":"2025-11-20T06:00:00","date_gmt":"2025-11-20T06:00:00","guid":{"rendered":"http:\/\/localhost:8080\/sk-hynix-redefines-its-vision-at-sk-ai-summit-2025-from-ai-memory-provider-to-creator\/"},"modified":"2026-06-15T08:56:16","modified_gmt":"2026-06-15T08:56:16","slug":"sk-hynix-redefines-its-vision-at-sk-ai-summit-2025-from-ai-memory-provider-to-creator","status":"publish","type":"post","link":"https:\/\/news.skhynix.com\/en\/sk-hynix-redefines-its-vision-at-sk-ai-summit-2025-from-ai-memory-provider-to-creator\/","title":{"rendered":"SK hynix Redefines Its Vision at SK AI Summit 2025: From AI Memory Provider to \u201cCreator\u201d"},"content":{"rendered":"

SK hynix shared its next-generation AI memory strategy and roadmap at the SK AI Summit 2025<\/a> held in Seoul from November 3\u20134. At the summit held under the theme \u201cAI Now & Next,\u201d the company elevated its vision from becoming a full-stack AI memory provider to embracing a broader role as a full-stack AI memory \u201ccreator.\u201d<\/p>\n

Hosted annually by SK Group, the event was formerly known as the SK Tech Summit but has evolved with a greater focus on AI to become the SK AI Summit since 2024. This year\u2019s event brought together global AI experts and industry professionals to discuss the present and future of AI technologies, as well as its applications across industries.<\/p>\n

Among those in attendance were SK Group Chairman Chey Tae-won, SK hynix CEO Kwak Noh-Jung, and SK Telecom CEO Jung Jaihun. They were joined by influential tech leaders including OpenAI CEO Sam Altman and Amazon CEO Andy Jassy, who delivered keynote speeches via video. Furthermore, global tech giants including NVIDIA, Amazon Web Services (AWS), Google, TSMC, and Meta, together with numerous domestic members of the K-AI Alliance* <\/sup>, participated to discuss collaboration opportunities across the AI ecosystem.<\/p>\n

* <\/sup>K-AI Alliance: Established by SK Telecom, the K-AI Alliance consists of Korean tech companies which aim to lead the development and global expansion of the Korean AI industry.<\/p>\n

The event featured keynote speeches, presentation sessions, and exhibitions, serving as a forum to explore the present and future of AI technology from multiple perspectives. As a leading innovator in AI infrastructure, SK hynix highlighted its technological leadership by presenting a memory technology roadmap for the AI expansion era and outlining its vision for collaboration across the broader industry.<\/p>\n

SK hynix CEO Kwak drew significant attention with a keynote speech on the evolving role and development path of the memory industry amid accelerating AI adoption. In addition, Senior Vice President Hoshik Kim of Memory System Research joined a keynote panel session to discuss solutions to AI infrastructure bottlenecks and the importance of memory-centric architectures. In sessions organized by SK hynix, Vice President Kyoung Park of Biz. Insight, Vice President Youngpyo Joo of System Architecture, and Professor Kyusang Lee of the University of Virginia shared insights on next-generation AI memory technologies, reinforcing the company\u2019s technological leadership.<\/p>\n

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\u25b2 SK Group Chairman Chey Tae-won presents the opening keynote at the SK AI Summit 2025<\/p>\n

SK Group Chairman Chey delivered the opening keynote under the theme \u201cAI Now & Next,\u201d setting the discussion around the next stage of the AI era. \u201cThe AI industry is now shifting from a race for scale to a race for efficiency,\u201d he said, adding that SK\u2019s role in responding to surging AI demand is to improve the efficiency of AI solutions. Chey emphasized: \u201cThe core of SK\u2019s AI strategy is working together with partners \u2014 Big Tech, governments, startups \u2014 to co-design and deliver the most efficient AI solutions. SK does not compete with its partners but creates AI business opportunities with them to find the most efficient AI solutions.\u201d<\/p>\n

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\u25b2(From first image) OpenAI CEO Sam Altman and Amazon CEO Andy Jassy delivering keynote addresses via video<\/p>\n

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CEO Kwak Presents Memory Vision for Expanding the AI Ecosystem<\/h3>\n
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On the first day of the summit, SK hynix CEO Kwak delivered a keynote speech titled \u201cSK hynix, New Vision & New Technology in the AI Era.\u201d Building on the vision presented last year, he refined it to meet the demands of the expanding AI era, providing more details on the directions for technological innovation and ecosystem collaboration.<\/p>\n

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\u25b2 SK hynix CEO Kwak Noh-Jung delivering a keynote speech at the summit<\/p>\n

\u201cAs the expansion of AI drives greater data movement, it has become crucial to respond by enhancing the performance of hardware,\u201d Kwak stated. \u201cHowever, the memory wall* <\/sup> has become a major obstacle to technological progress, as memory speeds fail to keep pace with advances in processors. Memory is no longer a simple component \u2014 it is becoming a key value product at the core of the AI industry. The issue is that the market\u2019s demands can no longer be met by the same level of performance improvements as before.\u201d<\/p>\n

* <\/sup>Memory wall: A bottleneck that occurs when memory access speed lags behind data processor speed.<\/p>\n

Kwak then outlined the company\u2019s redefined vision. \u201cUntil now, SK hynix has been on the path to become a full-stack AI memory provider that supplies the products our customers need at the right time,\u201d he said. \u201cGoing forward, we aim to evolve into a full-stack AI memory \u2018creator\u2019 that solves customers\u2019 problems and collaborates with the ecosystem to create greater value together. As a co-architect, partner and contributor to the ecosystem, SK hynix will design the future of AI infrastructure.\u201d<\/p>\n

He then unveiled SK hynix\u2019s full-stack AI memory strategy featuring three pillars: custom HBM* <\/sup>, AI DRAM (AI-D) and AI NAND (AI-N). First, custom HBM is designed to maximize performance by transferring certain compute functions, previously handled by GPUs and ASICs* <\/sup>, to the HBM\u2019s base die. This approach addresses evolving AI market demands, which are shifting from general-purpose performance to inference efficiency and total cost of ownership (TCO) optimization. Overall, custom HBM will enhance total system efficiency by improving GPU and ASIC compute efficiency, and reducing power consumption for data transfers.<\/p>\n

* <\/sup>High Bandwidth Memory (HBM): A high-value, high-performance memory product which vertically interconnects multiple DRAM chips and dramatically increases data processing speed in comparison to conventional DRAM products. There are six generations of HBM, starting with the original HBM followed by HBM2, HBM2E, HBM3, HBM3E, and HBM4.
\n* <\/sup>Application-specific Integrated Circuit (ASIC): Also known as a custom semiconductor, an ASIC is an integrated circuit designed for a specific purpose.<\/p>\n

Meanwhile, AI-D is a segmented DRAM product lineup optimized to meet the demands of the AI era beyond conventional general-purpose DRAM. SK hynix is developing:<\/p>\n

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  • AI-D O (Optimization):<\/strong> Low-power, high-performance DRAM that supports TCO reduction and operational efficiency for data centers. <\/em>Example products include MRDIMM, SOCAMM2, and LPDDR5R.<\/li>\n
  • AI-D B (Breakthrough):<\/strong> Ultra-high-capacity, highly flexible memory solutions designed to overcome the memory wall, such as CXL Memory Module (CMM) and Processing-in-Memory (PIM).<\/li>\n
  • AI-D E (Expansion):<\/strong> DRAM solutions such as HBM that have applications beyond data centers to fields including robotics, mobility and industrial automation.<\/li>\n<\/ul>\n

    Finally, AI-N is a next-generation storage solution lineup that combines high performance, high bandwidth and ultra-high capacity for AI systems. SK hynix is advancing its related NAND technology in three strategic directions, significantly enhancing its competitiveness in AI-targeted storage solutions:<\/p>\n

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    • AI-N P (Performance):<\/strong> Ultra-high-performance SSDs that significantly boost input\/output operations per second (IOPS) in large-scale AI inference environments through smaller chunk sizes. They can minimize bottlenecks between AI compute and storage, enhancing processing speed and energy efficiency. SK hynix is redesigning NAND and controllers under a new architecture and plans to release samples by the end of 2026.<\/li>\n
    • AI-N B (Bandwidth):<\/strong> High-capacity and cost-effective solutions that widen bandwidth by applying HBM-like stacked structures to NAND.<\/li>\n
    • AI-N D (Density):<\/strong> High-capacity solutions focused on storing large datasets with low power and minimal cost, suitable for AI data storage. <\/em>These QLC* <\/sup>-based solutions aim to achieve petabyte capacity, combining the speed of SSDs with the economy of HDDs.<\/li>\n<\/ul>\n

      * <\/sup>Quad-level cell (QLC): A type of memory cell used in NAND flash that stores four bits of data in a single cell. NAND flash is categorized as single-level cell (SLC), multi-level cell (MLC), triple-level cell (TLC), QLC, and penta-level cell (PLC) depending on how many data bits can be stored in one cell.<\/p>\n

      Wrapping up his keynote, Kwak added: \u201cSK hynix will continue to expand its role as a leading AI memory company by forming \u2018one-team\u2019 collaborations with global players across the AI industry to drive the next generation of AI.\u201d<\/p>\n

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      Discussing the Future of AI Infrastructure: Sharing Memory-Centric Tech Insights<\/h3>\n
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      \u25b2 (From first image) Hoshik Kim of Memory System Research; Kyoung Park of Biz. Insight; Youngpyo Joo of System Architecture; Professor Kyusang Lee of the University of Virginia<\/p>\n

      Hoshik Kim, Senior Vice President of Memory System Research at SK hynix, participated in a keynote panel session titled \u201cOvercoming the AI Infra Bottleneck: The Future Enabled by Memory-Centric Architecture.\u201d He was joined by David A. Patterson, professor emeritus at the University of California, Berkeley; Philip Wong, professor at Stanford University; Changkyu Kim, distinguished engineer at Meta; and Charles Fan, co-founder and CEO of MemVerge. Vice President Kim discussed the need for memory-centric architectures to scale AI compute performance and explored ways to establish a cooperative ecosystem across the industry.<\/p>\n

      Vice President Kyoung Park of Biz. Insight at SK hynix delivered a session titled \u201cThe Evolution of AI Service Infrastructure and the Role of Memory.\u201d Park noted that AI services are rapidly diversifying, ranging from cloud-based inference on massive models to lightweight models running on edge servers and devices. He emphasized that the role of memory is evolving beyond its traditional role as storage, emerging as a key resource that determines the performance of AI systems. Park also stated that memory technologies will become a critical driver of innovation in AI infrastructure.<\/p>\n

      Vice President Youngpyo Joo of System Architecture at SK hynix presented on \u201cThe Need and Direction of Collaboration With System-Level Partners From the Memory Solution Provider\u2019s Point of View.\u201d He stressed that close collaboration with ASIC and system vendors is essential to implement memory structures optimized for each workload\u2019s requirements. He also proposed an integrated approach that considers structural design, interfaces, power, and thermal characteristics.<\/p>\n

      Finally, Kyusang Lee, Professor at the University of Virginia, gave a talk titled \u201cEvolution Into Next-Generation CPO-based Connectivity Technology for Implementing Massive Computational Networks.\u201d He explained that current electrical interconnects face structural limitations in bandwidth and energy efficiency in large-scale AI computing networks and highlighted Co-Packaged Optics (CPO) as a key technology to overcome these limitations. He also pointed to micro-LED\u2013based light sources as a potential alternative to address heat and wavelength variation issues associated with laser light sources.<\/p>\n

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      Showcasing AI Memory Aligned With Key Presentations<\/h3>\n
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      \u25b2 SK Group\u2019s joint booth at the SK AI Summit 2025<\/p>\n<\/figure>\n

      At the SK Group joint booth operated under the concept of \u201cThe Next Depth,\u201d SK hynix showcased a wide range of innovative technologies designed to maximize AI infrastructure performance and efficiency.<\/p>\n

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      One of the highlights of the company\u2019s display was its 12-layer HBM4, which offers the high bandwidth and power efficiency required for ultra-high-performance AI computing. The company also displayed NVIDIA\u2019s next-generation GB300 Grace\u2122 Blackwell Superchip equipped with SK hynix\u2019s 36 GB HBM3E, the industry\u2019s highest capacity HBM on the market.<\/p>\n

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      \u25b2 A diverse range of DRAM solutions<\/p>\n<\/figure>\n

      In the DRAM module section, SK hynix presented various next-generation products for AI including RDIMM* <\/sup> and MRDIMM* <\/sup>solutions which leverage the 1c* <\/sup> node. The display also featured its 256 GB 3DS* <\/sup> RDIMM, 256 GB Tall MRDIMM, SOCAMM2* <\/sup>, LPCAMM2* <\/sup>, and CXL* <\/sup> Memory Module (CMM)\u2013DDR5 solutions optimized for platforms ranging from PCs to AI servers.<\/p>\n

      * <\/sup>Registered Dual In-line Memory Module (RDIMM): A server memory module product in which multiple DRAM chips are mounted on a substrate.
      \n* <\/sup>Multiplexed Rank Dual In-line Memory Module (MRDIMM): A server memory module product with enhanced speed by simultaneously operating two ranks \u2014 the basic operating units of the module.
      \n* <\/sup>1c: The 10 nm DRAM process technology has progressed through six generations: 1x, 1y, 1z, 1a, 1b, and 1c.
      \n* <\/sup>3D Stacked Memory (3DS): A high-performance memory solution in which two or more DRAM chips are packaged together and interconnected using TSV technology.
      \n* <\/sup>Small Outline Compression Attached Memory Module (SOCAMM): A low-power DRAM-based memory module specialized for AI servers.
      \n* <\/sup>Low Power Compression Attached Memory Module 2 (LPCAMM2): An LPDDR5X-based module solution that offers power efficiency and high performance as well as space savings. It delivers performance levels equivalent to two DDR5 SODIMMs.
      \n* <\/sup>Compute Express Link (CXL): A next-generation solution designed to integrate CPU, GPU, memory and other key computing system components \u2014 enabling unparalleled efficiency for large-scale and ultra-high-speed computations. Applying CXL to existing memory modules can expand capacity by more than 10 times.<\/p>\n

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      \u25b2 The memory-centric AI machine using\u00a0the CXL pooled memory (Niagara 2.0) solution<\/p>\n

      SK hynix also introduced a distributed Large Language Model (LLM) inference system connecting multiple servers and GPUs using pooled memory* <\/sup>, highlighting a shift to memory-centric architectures. The demonstration showed how the performance of LLM inference systems could be enhanced by using CXL pooled memory-based data communication over traditional network methods.<\/p>\n

      * <\/sup>Pooled Memory: A technology that groups multiple CXL memory units into a shared pool, allowing multiple hosts to efficiently allocate and share memory capacity. This approach significantly improves overall memory utilization.<\/p>\n

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      \u25b2 GDDR6-AiM and AiMX showcased by SK hynix<\/p>\n

      A separate demonstration highlighted how AiMX* <\/sup> is optimized for attention* <\/sup> operations \u2014 the core computation in LLMs. In a server equipped with AiMX cards and NVIDIA H100 GPUs, AiMX significantly enhanced the speed and efficiency of memory-bound* <\/sup> workloads. AiMX also improves the efficiency of KV-cache* <\/sup> utilization during long question-and-answer sequences, alleviating memory bottlenecks.<\/p>\n

      * <\/sup>AiM-based Accelerator (AiMX): SK hynix\u2019s accelerator card featuring GDDR6-AiM chips which is specialized for large language models (LLMs) \u2014 AI systems such as ChatGPT trained on massive text datasets.
      \n* <\/sup>Attention: An algorithmic technique that dynamically determines which parts of input data to focus on.
      \n* <\/sup>Memory-bound: A situation in which system performance is limited not by the processor\u2019s computational power but by memory access speed, causing delays while waiting for data access.
      \n* <\/sup>Key-Value (KV) cache: A technology that stores and reuses previously computed Key and Value vectors, preserving context from earlier inputs to reduce redundant calculations and respond more efficiently.<\/p>\n

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      \u25b2 Various eSSD products from SK hynix<\/p>\n<\/figure>\n

      SK hynix also introduced its high-performance eSSD* <\/sup> lineup including PS1010 E3.S, PEB110 E1.S, and PS1101 E3.L. Optimized for computing and storage servers, the eSSD portfolio enhances data processing efficiency in AI service infrastructures and storage utilization in large-scale computing environments.<\/p>\n

      * <\/sup>Enterprise Solid-State Drive (eSSD): An enterprise-grade SSD used in servers and data centers.<\/p>\n

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      \u25b2 The wafer notch angle detection system<\/p>\n<\/figure>\n

      In the AIX zone, visitors could check out the wafer notch angle detection system. This system applies the YOLOv8-OBB deep learning model inside semiconductor chambers* <\/sup> to detect wafer notches in real time with high precision. It drew attention for its ability to overcome the limitations of conventional sensor-based methods, prevent process defects in advance, and reduce equipment maintenance costs.<\/p>\n

      * <\/sup>Semiconductor chamber: A sealed environment within semiconductor manufacturing equipment where specific processes are performed. For example, in the etching process, the chamber serves as the space where semiconductor wafers are processed. Semiconductor manufacturing equipment typically consists of multiple chambers that perform various tasks throughout the production process.<\/p>\n

      At the SK AI Summit 2025, SK hynix unveiled a bold new vision built on collaboration and innovation which will take its AI memory to new heights. \u201cSK hynix will pioneer the future of AI memory together with our customers, partners and the broader ecosystem,\u201d said SK hynix CEO Kwak. \u201cWe place customer satisfaction as our highest priority and will generate greater synergy with our global partners to lead the next era of AI.\u201d<\/p>\n

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