Korea's AI Infrastructure Landscape: Navigating Growth, Competition, and Policy

1. Executive Summary

• This report analyzes the rapidly evolving AI infrastructure landscape in South Korea, focusing on the interplay between government policy, private sector investment, technological innovation, and competitive dynamics. Korea's commitment to AI is evident in its ambitious GPU procurement targets and strategic initiatives for semiconductor localization.

• Key findings reveal NVIDIA's dominance in the GPU market (92% share in Q1 2025), balanced against AMD's efforts to gain ground with its MI300X series. The report emphasizes the criticality of high-density storage solutions (petabyte-scale per rack unit) and advanced networking (400G+ Ethernet) to support AI workloads. Scenario planning projects varying growth trajectories for AI data centers, contingent on GPU procurement pace and cloud AI service adoption. Strategic recommendations include proactive government subsidies for domestic HBM production, and a cost-benefit framework for enterprises evaluating colocation vs. public cloud. Ultimately, Korea's success hinges on its ability to balance foreign technology reliance with domestic innovation.

2. Introduction

• South Korea is strategically positioning itself as a global leader in artificial intelligence. This ambition requires a robust and advanced AI infrastructure, encompassing high-performance computing, scalable storage, and efficient networking. But several questions emerge: How can Korea secure its AI infrastructure supply chains, particularly in the face of global competition and geopolitical uncertainties?

• This report provides a comprehensive analysis of Korea's AI infrastructure landscape, examining key trends in data centers, government policies, private sector investments, and technological advancements. The report investigates the competitive dynamics between major players like NVIDIA and AMD, the role of emerging technologies like Ultra Ethernet 1.0, and the challenges of ensuring energy efficiency and sustainability.

• The core objective is to provide stakeholders – government policymakers, private sector investors, and technology providers – with actionable insights to navigate the complexities of this rapidly evolving landscape. This includes understanding the risks and opportunities associated with GPU supply chains, optimizing data center deployment strategies, and aligning infrastructure development with broader national goals.

• The report begins by establishing the fundamentals of AI data centers, highlighting their unique requirements compared to traditional facilities. It then delves into government policies and private sector investments, followed by an analysis of the GPU supply chain and the competition between NVIDIA and AMD. Subsequent sections explore storage and network solutions, energy efficiency strategies, and future growth scenarios. The report concludes with strategic recommendations for stakeholders to maximize ecosystem impact.

3. AI Data Center Fundamentals: Architectural Distinctions and Performance Metrics

• 3-1. Differentiation from General Data Centers

• This subsection establishes the fundamental differences between AI-specific data centers (AI-DCs) and general-purpose data centers, focusing on the heightened demands for compute density, low latency, and scalable storage. These differentiations are critical for understanding the specialized infrastructure investments required in Korea and setting the stage for subsequent sections analyzing government policies and private sector strategies.

• AI data centers (AI-DCs) require significantly higher power densities compared to traditional data centers due to the intensive computational demands of AI workloads. While typical data centers operate at 10-14 kW/rack, AI servers can demand 20-40 kW/rack, and advanced systems like NVIDIA's H100-based architectures can reach up to 40kW per rack, highlighting a substantial increase in energy requirements (ref_idx 70). Meeting these demands poses a challenge for existing infrastructure, especially in Korea, where space and energy resources are constrained.

• Several factors contribute to the high power density in AI-DCs. The dominance of GPUs and TPUs over CPUs requires high-performance computing (HPC) infrastructure capable of large-scale parallel processing (ref_idx 4, 5). Additionally, high-speed networking and low-latency requirements necessitate advanced interconnect technologies, increasing power consumption. To effectively manage this, AI-DCs need innovative cooling solutions, such as liquid cooling, to dissipate the heat generated by high-density servers, which existing air-cooling systems cannot adequately handle (ref_idx 1, 70).

• SK Telecom is implementing a power density of 44kW per rack at its Gasan data center, positioning it as a leader in Korea (ref_idx 321). This high density is essential for supporting GPU servers efficiently. High Voltage Direct Current (HVDC) architecture combined with high-density multi-phase solutions are setting new standards, promoting the development of high-quality components and power distribution systems (ref_idx 145, 146). High rack densities, potentially rising to 600kW per rack by 2027, are becoming an issue because of increased power consumption and the necessity for contributing to capacity expansion and grid resilience (ref_idx 151).

• The strategic implication is that Korean AI-DC operators must invest in advanced power and cooling infrastructure to support high-density AI workloads. Liquid cooling solutions, such as direct liquid cooling and immersion cooling, are becoming essential for maintaining performance and efficiency (ref_idx 1, 147). Furthermore, integrating renewable energy sources and implementing workload choreography can reduce carbon intensity and improve energy efficiency (ref_idx 42, 43, 64).

• Recommendations include accelerating the adoption of liquid cooling technologies, developing partnerships with renewable energy providers, and implementing dynamic workload management to optimize energy consumption. Government support for R&D in these areas is crucial for fostering innovation and ensuring that Korean AI-DCs remain competitive.

• Low latency is critical for AI data centers to support real-time data processing and analysis, particularly for applications like autonomous vehicles, financial modeling, and natural language processing. General data centers, focused on general transaction processing, do not necessitate ultra-low latency networks; AI-DCs, however, require high-performance networks with speeds of 400Gbps+ and microsecond-level latency to facilitate real-time insights and rapid decision-making (ref_idx 4, 5).

• The key mechanism driving low latency is the shift towards advanced network architectures, including ultra-fast Ethernet and in-network acceleration. These technologies minimize data transmission delays and optimize network performance. The plan set theoretical latency target no greater than 1.5 times the direct network transmission for national data center clusters, less than 5ms between important computing infrastructure within national hub nodes, and below 1ms between key infrastructure in urban areas (ref_idx 221).

• AWS-SK Group’s AI data center in Ulsan deploying UltraCluster networking exemplifies the drive for low-latency connectivity in Korea (ref_idx 28). The adoption of colocation strategies and multi-cloud integration further optimizes network performance, ensuring minimal latency for AI workloads. Cisco-NVIDIA In-Network Acceleration solutions also showcase advancements in high-speed networking to reduce latency and improve data processing speeds (ref_idx 41).

• The strategic implication is that Korean AI-DC operators must prioritize investments in high-speed, low-latency network infrastructure to meet the stringent requirements of AI applications. This includes deploying advanced Ethernet technologies, optimizing network topologies, and leveraging in-network acceleration solutions. Additionally, the network should adopt innovative technologies such as RFID, IPv6 and SRv6 to achieve an OTN coverage rate of 80% in key application areas and to increase the usage of innovative technologies to 40% (ref_idx 221).

• Recommendations include establishing partnerships with network technology providers, implementing end-to-end network monitoring and optimization tools, and adopting a software-defined networking (SDN) approach to dynamically manage network resources. Focus on the average Water Usage Effectiveness (WUE) and Carbon Usage Effectiveness (CUE) to meet the objective reduce the average Power Usage Effectiveness (PUE) of data centers to below 1.5 by the end of 2025 (ref_idx 221).

• AI workloads demand massive storage capacity and high-speed data access to support the training and deployment of large AI models. Traditional data centers, primarily focused on data storage and transaction processing, offer insufficient storage capabilities for AI applications (ref_idx 4, 5). Korean AI-DCs, therefore, need to provide ZB-scale storage targets with high-throughput capabilities to handle the ever-growing data volumes.

• The key mechanism for achieving high storage density is the adoption of all-flash storage arrays and NVMe-based solutions. These technologies provide significantly lower latency and higher bandwidth compared to traditional HDD-based storage systems (ref_idx 40). Advanced storage power accounting for over one-third, and achieve total disaster recovery coverage for key industry core data and other crucial data should be achieved (ref_idx 221).

• VAST Data's DGX-A100 reference architecture exemplifies the use of high-density storage solutions in AI-DCs, showcasing the scalability and performance of all-flash arrays (ref_idx 40). Transition from NVMe to HDD showcases the importance of low-latency trade-offs for data-intensive AI applications. SK Telecom is also investing in high-performance storage solutions to support its AI infrastructure initiatives (ref_idx 97, 99).

• The strategic implication is that Korean AI-DC operators must invest in scalable and high-performance storage infrastructure to support the data-intensive nature of AI workloads. This includes deploying all-flash arrays, NVMe-based storage systems, and software-defined storage solutions to optimize storage utilization and performance.

• Recommendations include partnering with leading storage vendors, implementing data compression and deduplication technologies to maximize storage efficiency, and adopting a tiered storage approach to balance cost and performance. The goal is to exceed 1800 EB, with advanced storage power accounting for over one-third, and to achieve total disaster recovery coverage for key industry core data and other crucial data (ref_idx 221).

• 3-2. Key Design Patterns and Metrics

• This subsection aims to establish quantifiable benchmarks for AI data center deployments in Korea, focusing on PFLOPS per compute module, ZB-scale storage targets, and PUE efficiency targets. By setting concrete performance indicators, this subsection provides a framework for evaluating the current state and future advancements of AI infrastructure in Korea, directly addressing the need for detailed metrics to guide strategic decision-making.

• AI data centers demand high-performance computing (HPC) infrastructure capable of large-scale parallel processing, necessitating the use of advanced GPUs or TPUs. Quantifying the performance of these compute modules in terms of PFLOPS (Peta Floating-point Operations Per Second) is crucial for benchmarking and optimizing AI workloads (ref_idx 4). While general data centers focus on CPU-based computations, AI-DCs require specialized hardware that can deliver significantly higher PFLOPS per module.

• The key mechanism for achieving high PFLOPS per module involves employing advanced processor architectures and interconnect technologies. For instance, NVIDIA's Blackwell architecture promises substantial performance gains, and AMD's MI300X series also aims to deliver competitive PFLOPS (ref_idx 437). Structured sparsity techniques further enhance performance by optimizing computational efficiency, while full-flat network architectures improve scalability for large language models (ref_idx 435).

• Although specific PFLOPS figures for Korean AI-DCs are not explicitly stated in the provided documents, Samsung has recently introduced 'SSC-24', a supercomputer with a performance of 106.2 PFLOPS, ranking 18th globally and 1st in Korea (ref_idx 438, 439). This indicates a significant investment in high-performance computing infrastructure. Furthermore, SK Telecom and AWS are collaborating to build Korea's largest AI data center with plans for deploying 60,000 GPUs, suggesting a substantial commitment to high compute capacity (ref_idx 97, 148).

• The strategic implication is that Korean AI-DC operators must focus on maximizing PFLOPS per module to efficiently handle the computational demands of AI applications. This requires careful selection of GPU/TPU hardware, optimization of network architectures, and implementation of advanced software techniques such as structured sparsity and workload choreography.

• Recommendations include conducting thorough performance benchmarking of different compute modules, investing in high-bandwidth memory (HBM) capacity, and leveraging open-source software frameworks like ROCm to optimize performance and scalability (ref_idx 437). The Ministry of Science and ICT also plans to support teams with 10,000 GPUs, secured by the first supplementary budget (ref_idx 222).

• AI workloads, particularly the training and deployment of large language models (LLMs), demand massive storage capacity with high-speed data access. Traditional data centers, focused on data storage and transaction processing, offer insufficient storage capabilities for AI applications. Korean AI-DCs, therefore, need to provide ZB-scale storage targets to handle the ever-growing data volumes (ref_idx 4). The global data volume is projected to rise to 181 zettabytes by the end of 2025, underscoring the need for advanced storage solutions (ref_idx 483).

• The key mechanism for achieving high storage density is the adoption of all-flash storage arrays and NVMe-based solutions. These technologies provide significantly lower latency and higher bandwidth compared to traditional HDD-based storage systems. Furthermore, integrating data management and migration tools is crucial for efficiently managing large datasets and optimizing storage utilization (ref_idx 488).

• While precise ZB-scale storage forecasts specific to Korean AI-DCs are not provided in the reference documents, several reports highlight the exponential growth of data worldwide. For instance, one estimate suggests that humankind will generate 2,537 zettabytes of data by 2030 (ref_idx 484). Given Korea's strong focus on AI development and the expansion of AI infrastructure, it's reasonable to assume that Korean AI-DCs will contribute significantly to this global trend.

• The strategic implication is that Korean AI-DC operators must invest in scalable and high-performance storage infrastructure to support the data-intensive nature of AI workloads. This includes deploying all-flash arrays, NVMe-based storage systems, and software-defined storage solutions to optimize storage utilization and performance.

• Recommendations include partnering with leading storage vendors, implementing data compression and deduplication technologies to maximize storage efficiency, and adopting a tiered storage approach to balance cost and performance. Consider utilizing HPE file storage as one option (ref_idx 482). Also, promote disaster recovery coverage for crucial data (ref_idx 221).

• Energy efficiency is a critical concern for AI data centers due to the high power consumption of AI workloads. PUE (Power Usage Effectiveness) is a key metric for measuring data center energy efficiency, defined as the ratio of total energy used in a data center to the energy used by IT equipment. A lower PUE indicates higher energy efficiency, with an ideal target of 1.0 (ref_idx 549). Korean AI-DCs must strive for PUE efficiency targets of <1.2 to minimize energy costs and reduce their environmental footprint.

• The key mechanism for achieving low PUE involves optimizing cooling systems, implementing efficient power distribution, and integrating renewable energy sources. Advanced cooling technologies, such as liquid cooling and direct free cooling, can significantly reduce energy consumption compared to traditional air-cooling systems. Furthermore, workload choreography and dynamic workload management can optimize energy consumption by shifting workloads to times when renewable energy is more available (ref_idx 64).

• While the average PUE for Korean data centers was 2.54 in 2016, which is higher than the global average (ref_idx 68), there are examples of more efficient data centers in Korea. NHN Cloud Center (NCC) maintains a PUE of 1.2-1.3 by applying various energy-saving technologies (ref_idx 547). Samsung SDS also reports PUE values for its data centers, with the Dongtan Data Center achieving a PUE of 1.14 (ref_idx 550). These examples demonstrate that achieving PUE targets below 1.2 is feasible in Korea.

• The strategic implication is that Korean AI-DC operators must prioritize energy efficiency to remain competitive and meet sustainability goals. This requires investing in advanced cooling technologies, optimizing power distribution, and integrating renewable energy sources. Government support for R&D in these areas is crucial for fostering innovation and ensuring that Korean AI-DCs remain competitive.

• Recommendations include adopting liquid cooling technologies, implementing dynamic workload management, and partnering with renewable energy providers. Additionally, participating in the Green Data Center certification program can provide a framework for improving energy efficiency and achieving recognition for sustainable practices (ref_idx 546).

4. Government-Led AI Infrastructure Strategy and Semiconductor Localization

• 4-1. Policy Framework and GPU Procurement

• This subsection delves into the Korean government's strategic initiatives for securing GPU resources, focusing on the policy frameworks and procurement plans designed to bolster the nation's AI capabilities. It builds upon the previous section's foundation of AI data center distinctions, setting the stage for analyzing the government's role in shaping AI infrastructure.

• The Korean government has earmarked a substantial budget for acquiring 10,000 GPUs by 2025, signaling a strong commitment to enhancing AI infrastructure (ref_idx 2, 154, 155). This initiative is driven by the need to provide sufficient compute resources for AI model training and deployment, crucial for both public and private sector innovation. The scale of this investment reflects the urgency with which the government views the AI landscape.

• The government's procurement strategy involves collaborating with cloud service providers (CSPs) to manage and operate the acquired GPUs (ref_idx 154, 155, 157). This approach allows for efficient resource allocation and leverages the expertise of CSPs in managing large-scale computing infrastructure. The government retains ownership of the GPUs while outsourcing the operational aspects to private entities, creating a public-private partnership model.

• Several major players, including NHN Cloud, Naver Cloud, Kakao, and Coupang, are vying for the opportunity to participate in this GPU procurement and deployment initiative (ref_idx 154, 155, 157, 159). These companies possess varying degrees of experience in cloud services and AI data center operations, making the competition intense. The government's selection process will likely prioritize factors such as data center infrastructure, GPUaaS capabilities, and AI ecosystem contributions.

• The government's investment of 1.46 trillion won (approximately $1.04 billion USD) underscores the significance of this project and the scale of the intended GPU deployment (ref_idx 154, 155, 168). Securing this level of compute power is critical for supporting national AI initiatives, fostering innovation, and competing with global AI leaders. The economic impact extends beyond mere hardware acquisition, fostering a broader ecosystem of AI development and services.

• To ensure effective utilization and prevent bottlenecks, the government should focus on providing robust support for AI researchers and developers, including access to comprehensive documentation, training resources, and technical assistance. Creating a user-friendly environment will encourage experimentation, accelerate model development, and maximize the return on investment in GPU infrastructure.

• A key aspect of the government's AI infrastructure strategy involves the creation of institutional clusters dedicated to AI research and development (ref_idx 2). This clustering approach aims to foster collaboration, knowledge sharing, and resource pooling among researchers and institutions. By concentrating expertise and infrastructure, the government seeks to accelerate AI innovation and address critical challenges.

• These AI clusters are intended to serve as hubs for talent development, attracting and retaining skilled AI professionals and researchers. The government plans to expand AI graduate programs and establish AI convergence degree programs to cultivate a pipeline of qualified experts. Furthermore, the clusters will offer opportunities for industry-academia collaboration, enabling students and researchers to gain practical experience and contribute to real-world AI projects.

• To facilitate effective collaboration, the government should invest in creating shared research facilities, data repositories, and software platforms within these AI clusters. This will reduce duplication of effort, promote interoperability, and enable researchers to build upon existing knowledge and resources. Furthermore, the government should encourage open-source initiatives and the sharing of AI models and datasets to foster innovation and accelerate progress.

• The success of AI clusters depends on robust network infrastructure and high-speed connectivity (ref_idx 231). Investing in advanced networking technologies, such as 400Gbps Ethernet and beyond, is essential for enabling seamless data transfer and communication between cluster nodes. Additionally, the government should prioritize the deployment of low-latency connections to minimize delays and maximize the efficiency of distributed AI workloads.

• Successful implementation requires active engagement from government agencies, academic institutions, and private sector partners to align research priorities with national needs and market demands. The government should play a proactive role in coordinating cluster activities, facilitating communication, and providing support for technology transfer and commercialization efforts.

• Recognizing the critical importance of thermal management in AI data centers, the Korean government is providing R&D support for cold plate cooling technologies and other thermal management solutions (ref_idx 2). These efforts are aimed at mitigating the heat generated by high-density GPU deployments, ensuring stable operation, and improving energy efficiency.

• Cold plate cooling involves direct liquid cooling of heat-generating components, offering superior heat dissipation compared to traditional air cooling methods (ref_idx 71, 349, 352). By precisely targeting heat sources, cold plates can maintain optimal operating temperatures, reduce the risk of overheating, and improve the overall reliability of AI systems. This technology is particularly well-suited for high-performance computing environments where heat density is a major concern.

• Given the reliance on imported non-conductive coolants for immersion cooling (ref_idx 71), the government should prioritize R&D efforts focused on developing eco-friendly, domestic alternatives, aiming to reduce reliance on imports and promote a more sustainable approach to data center cooling. Furthermore, domestic production could offer cost advantages and enhance supply chain security.

• To accelerate the adoption of advanced cooling technologies, the government should establish industry standards, promote best practices, and provide incentives for data center operators to adopt energy-efficient cooling solutions. This will encourage investment in innovative technologies, improve overall data center efficiency, and reduce the environmental impact of AI infrastructure.

• Effective implementation requires government agencies, research institutions, and private sector partners to collaborate on R&D projects and share knowledge and expertise. This collaborative approach will facilitate the development of cutting-edge cooling technologies, ensure their compatibility with existing infrastructure, and accelerate their deployment in AI data centers across the country.

• 4-2. HBM and AI Chip Ecosystem Development

• This subsection analyzes the Korean government's long-term strategy for achieving semiconductor self-reliance, particularly in HBM production and AI chip ecosystem development. Building on the previous discussion of policy frameworks and GPU procurement, this section focuses on the roadmap for reducing dependence on foreign technologies and fostering a domestic AI semiconductor industry.

• The Korean government aims to bolster its HBM production capabilities to mitigate reliance on foreign suppliers and ensure a stable supply for domestic AI initiatives. Currently, Samsung and SK Hynix dominate the HBM market, holding a significant share in the NVIDIA GPU-coupled ecosystem (ref_idx 7, 444). The government seeks to leverage this existing strength to expand into AI-specific processors and system semiconductors, thereby enhancing the overall competitiveness of the semiconductor industry.

• Micron's entry into the HBM market with its HBM3E product poses a competitive challenge, but also underscores the growing demand for HBM (ref_idx 443, 450). While Micron has secured supply agreements with NVIDIA, Samsung and SK Hynix are ramping up their HBM3E production and developing HBM4 technology to maintain their market leadership (ref_idx 444, 448). TrendForce projects that HBM3E will become mainstream by next year, driven by the adoption of AI platforms.

• To increase domestic HBM production, both Samsung and SK Hynix are expanding their manufacturing capacity (ref_idx 454, 494). Samsung is building a new packaging line in Cheonan to increase HBM production and aims to double its production capacity compared to this year. SK Hynix is focusing on expanding its TSV lines in Cheongju to increase HBM output. This expansion is critical for meeting the growing demand and achieving greater self-sufficiency.

• The government should provide incentives for companies to invest in HBM production facilities and R&D, including tax breaks, subsidies, and streamlined regulatory processes (ref_idx 7, 571). Furthermore, fostering collaboration between industry, academia, and research institutions will accelerate innovation and improve HBM production efficiency.

• Establishing clear, measurable targets for HBM production capacity and market share will help track progress and ensure accountability. Regular assessments of the HBM supply chain and potential bottlenecks will enable proactive measures to address challenges and maintain a stable supply for domestic AI development.

• Achieving HBM self-sufficiency requires setting ambitious yet realistic annual targets for domestic production to reduce reliance on foreign sources. While precise figures for HBM self-sufficiency targets are not explicitly stated, the government's overall objective is to decrease dependency on imported semiconductors, particularly from NVIDIA (ref_idx 7). Diversifying the HBM supply chain is crucial to mitigating risks associated with geopolitical tensions and trade restrictions.

• To enhance supply chain stability, the government should foster partnerships with multiple HBM suppliers, encouraging competition and reducing reliance on any single vendor. This can be achieved through financial incentives, technology transfer programs, and joint R&D projects with domestic and international partners (ref_idx 573, 578).

• The government should actively monitor the HBM market landscape, track production yields, and identify potential disruptions in the supply chain. Developing contingency plans and diversifying sourcing strategies will ensure a stable supply of HBM for domestic AI initiatives even in the face of unforeseen challenges (ref_idx 572, 580).

• Creating a national HBM stockpile can serve as a buffer against supply disruptions and ensure the availability of critical components for strategic AI projects. This stockpile can be managed through a public-private partnership, with the government providing financial support and oversight while private companies manage the storage and distribution of HBM.

• Implementing robust quality control measures and establishing industry standards for HBM manufacturing will ensure the reliability and performance of domestically produced components. This will build trust in local HBM suppliers and encourage wider adoption within the domestic AI ecosystem.

• Developing domestic NPU capabilities is essential for reducing reliance on foreign GPUs and creating a more self-sufficient AI ecosystem. The Korean government has allocated significant funding to support NPU development and commercialization, aiming to foster a vibrant ecosystem of domestic AI chip companies (ref_idx 7, 571).

• To encourage domestic NPU development, the government should provide grants, tax incentives, and R&D funding to promising AI chip startups and established semiconductor companies. Establishing a dedicated fund for AI chip development will attract private investment and accelerate innovation (ref_idx 574, 577, 581).

• Currently, domestic AI development heavily relies on NVIDIA GPUs, but the government aims to reduce this dependency by actively supporting the development and validation of AI-specific NPUs (ref_idx 7, 575). The Ministry of Science and ICT is investing ₩49.4 billion in a supplementary budget to support the commercialization of domestic AI semiconductors, with a total investment of ₩243.4 billion in the AI semiconductor sector this year. This includes establishing AI computing demonstration infrastructure to support domestic fabless companies in early commercialization.

• Successful development and commercialization require partnerships between government agencies, research institutions, and private sector companies. Government should facilitate collaboration, promote knowledge sharing, and offer support for technology transfer and commercialization efforts (ref_idx 579, 582).

• Setting clear performance targets, establishing testing and certification standards, and promoting the adoption of domestic NPUs in government projects will create a demand for local AI chips and accelerate their market penetration. This targeted approach will ensure that domestic NPUs meet the needs of the Korean AI ecosystem and contribute to the nation's technological sovereignty.

5. Private Sector Investment Dynamics: AWS-SK AI Zone and Alibaba Expansion

• 5-1. AWS-SK Collaboration in Uljanik

• This subsection delves into the AWS-SK collaboration in Uljanik, providing a detailed case study of their greenfield AI data center development. It builds upon the foundational understanding of AI data center requirements established in the previous section and sets the stage for evaluating multi-cloud adoption trends by analyzing the power capacity and GPU density of this significant project. This section acts as a bridge connecting general concepts to a concrete, large-scale deployment.

• While the AWS-SK collaboration in Uljanik promises 7.8k job creation (ref_idx 28), assessing the true scale of the AI infrastructure requires understanding its power capacity. The initially announced 103 MW capacity (ref_idx 94, 95) suggests a substantial commitment to high-density computing infrastructure, moving beyond mere job creation figures to indicate a significant build-out of AI-specific hardware.

• Power capacity directly correlates with the number of high-powered GPUs that can be deployed. Analyzing power infrastructure enables a deeper understanding of the data center's ability to support computationally intensive AI workloads. Considering the power demands of modern GPUs, the 103 MW capacity points to a significant concentration of processing power aimed at accelerating AI development and deployment in Korea.

• Multiple reports (ref_idx 94, 97, 99) confirm the 103 MW target, further solidifying the scale of investment. SK Group's advanced energy solutions and stable gas supply network in Ulsan are key enablers for supporting this large power draw (ref_idx 96, 101). The collocation of the data center with SK Gas’s LNG power plant guarantees reliable power delivery which is essential for large scale AI infrastructure (ref_idx 97).

• Quantifying power capacity contextualizes job creation claims. A large power capacity indicates a commitment to supporting energy-intensive AI workloads. The 103MW capacity indicates a top-tier facility designed to push the boundaries of AI compute capabilities. Future developments should focus on maximizing power utilization effectiveness through optimized cooling and energy management strategies.

• Recommendations include leveraging LNG cold energy for cooling (ref_idx 103, 104) and incorporating advanced power management solutions to improve overall efficiency and reduce operational costs. Government incentives should promote energy-efficient data center designs, facilitating the deployment of sustainable AI infrastructure.

• Beyond power capacity, understanding the planned GPU rack count provides a more granular view of the Ulsan AI Zone’s computing capacity. While the total number of 60,000 GPUs is widely reported (ref_idx 94, 97, 98), the rack density reveals critical insights into the data center's design and operational efficiency. A higher rack density implies better utilization of space and infrastructure resources.

• Estimating rack count is crucial. Assuming an average of 4-8 GPUs per server and a rack accommodating 20-40 servers, the 60,000 GPUs would translate to approximately 750 to 3000 racks. This range highlights the potential variability in rack density and the overall physical footprint required for the AI zone.

• Reports emphasize the deployment of 60,000 GPUs, the largest GPU deployment in a Korean data center (ref_idx 97, 98, 99, 100). This suggests that SK and AWS are aiming to create a leading-edge AI compute environment. SK Telecom’s earlier announcement about hyper-scale AI data center (ref_idx 102) with 60,000 GPUs through global big tech partnerships, validates the claim.

• Establishing GPU rack count allows benchmarking against other AI-DCs. Comparing the rack density and GPU count with global standards helps in assessing the competitive positioning of the Ulsan AI Zone. A high GPU rack count indicates the data center's focus on supporting computationally demanding AI workloads, attracting AI researchers and industry partners.

• Future efforts should be directed towards optimizing GPU rack density by leveraging advanced cooling solutions and rack designs. Investing in high-density racks and efficient cooling systems maximizes the computing power within limited space and minimizes operational costs. Close monitoring of utilization metrics will be crucial for realizing performance gains from infrastructure investments.

• 5-2. Multi-Cloud AI Adoption Trends

• This subsection builds on the AWS-SK Ulsan AI Zone case study by broadening the scope to multi-cloud adoption trends, focusing on Alibaba's expansion in South Korea. It evaluates the cost-optimization strategies employed by enterprises, particularly the trade-offs between colocation and public cloud for AI workloads. This analysis contributes to a comprehensive understanding of private sector investment dynamics in the Korean AI infrastructure landscape.

• Alibaba Cloud's launch of its second data center in Seoul signals a strategic move to capture a larger share of the burgeoning South Korean AI infrastructure market (ref_idx 58, 312). With AI service demand skyrocketing, Alibaba aims to provide optimized solutions for cloud-native, big data, and database applications, positioning itself as a key player in the multi-cloud ecosystem.

• While specific capacity figures (in MW) for Alibaba's SK data center in South Korea are not explicitly detailed in the provided documents, the company emphasizes that this expansion is designed to meet the escalating demand for AI-optimized infrastructure across various emerging markets, moving beyond traditional cloud computing requirements (ref_idx 58). This implies a substantial investment in high-density computing capabilities.

• Alibaba's strategy includes partnering with local IT and cloud service providers like Megazone Soft, Itechsystem, and Ayitisencloyit to offer tailored consulting services and industry-specific solutions (ref_idx 312). This approach enables Alibaba to cater to the unique needs of Korean enterprises adopting multi-cloud strategies, fostering a diverse and competitive cloud landscape.

• The deployment of a second data center enhances Alibaba's redundancy and regional presence, enabling it to compete more effectively with established global providers like AWS, Microsoft, and Google Cloud, as well as domestic players such as Naver Cloud and Kakao Enterprise (ref_idx 58, 313). This investment underscores Alibaba's long-term commitment to the South Korean market.

• Recommendations include focusing on energy efficiency and sustainability to differentiate Alibaba's offerings and align with South Korea's green initiatives. Further investments in specialized AI hardware and software solutions can enhance its competitive edge and attract enterprises seeking cost-effective and high-performance AI infrastructure.

• The increasing costs associated with AI inferencing are prompting organizations to reassess their cloud strategies, with many exploring colocation and specialized hosting providers as alternatives to the big public cloud operators like AWS, Azure, and Google Cloud (ref_idx 60, 63). Canalys reports that public clouds may become unsustainable from a cost perspective as AI deployments scale, leading to a search for more cost-effective solutions.

• Inferencing costs, which are often usage-based (e.g., per token or API call), can be difficult to predict, leading to budget overruns and potentially hindering AI adoption (ref_idx 60, 422, 426). Companies are seeking pricing structures that offer better resource handling and cost control, enabling them to deploy AI applications without the risk of unexpected expenses. 37Signals case serves as example of cloud cost exceeding expectation (ref_idx 422).

• Reports indicate that while the top cloud service providers still dominate the market, their growth rates are diverging, with AWS experiencing a slowdown while Microsoft and Google maintain higher growth rates (ref_idx 60, 63). This suggests that businesses are increasingly considering cost-effectiveness and tailored solutions offered by specialized providers and Rakuten Mobile shifted from Public Cloud to Private Cloud due to ROI consideration (ref_idx 424).

• Colocation offers the benefit of predictable pricing and greater control over infrastructure, allowing enterprises to optimize resource allocation and potentially reduce costs for AI workloads, especially inferencing (ref_idx 427, 433). However, it also requires significant upfront investment and ongoing management expertise.

• Recommendations include conducting thorough cost-benefit analyses to determine the optimal deployment strategy for AI workloads, considering factors such as performance requirements, data security, and regulatory compliance. Exploring hybrid cloud models that combine the flexibility of public cloud with the cost-effectiveness of colocation can provide a balanced approach to AI infrastructure management.

6. GPU Supply Chain Competition: NVIDIA Dominance vs. AMD Incursion

• 6-1. Market Share and Product Leadership

• This subsection delves into the competitive dynamics of the GPU market, focusing on NVIDIA's commanding lead and AMD's attempts to gain ground. It builds upon the preceding section's exploration of private sector investments by examining how these investments translate into market share and technological advantages, setting the stage for the subsequent discussion of storage and network solutions influenced by these competitive forces.

• In the first quarter of 2025, NVIDIA solidified its position as the dominant player in the data center GPU market, capturing a staggering 92% market share according to Jon Peddie Research (ref_idx 49, 50). This represents a significant increase from the previous quarter, highlighting NVIDIA's continued success in meeting the surging demand for AI compute. AMD, despite launching new products, saw its market share decline to a historical low of 8%, signaling challenges in competing effectively with NVIDIA's established ecosystem and supply chain.

• NVIDIA's success can be attributed to its robust portfolio of high-performance GPUs, particularly the H100 and A100, which have become the industry standard for AI training and inference workloads. These GPUs are backed by NVIDIA's CUDA platform, providing developers with a comprehensive set of tools and libraries to accelerate AI development. The strong demand for NVIDIA's GPUs has led to substantial revenue growth, with data center GPU sales rising significantly from 2016 to 2022, and then exploding from 2023 onward due to the AI infrastructure race sparked by ChatGPT (ref_idx 208).

• AMD's struggles in gaining market share stem from a combination of factors, including difficulty in predicting demand accurately and balancing TSMC allocations between CPUs and GPUs (ref_idx 217). While AMD has launched competitive products like the RX 9000 series, these have not been enough to overcome NVIDIA's lead. A ComputerBase survey indicated that AMD shipped twice as many RX 9000 GPUs as NVIDIA’s RTX 5000 series, but NVIDIA’s Blackwell gaming GPUs were released much earlier than AMD’s RDNA 4 lineup but still fell behind (ref_idx 56).

• The implications of NVIDIA's dominance are significant for the Korean AI infrastructure market. Korean companies relying on AI compute may face higher costs and limited GPU availability due to NVIDIA's pricing power and supply constraints. Furthermore, the lack of competition could stifle innovation and prevent the development of alternative GPU architectures. To mitigate these risks, Korean policymakers should actively support AMD and other GPU vendors to foster a more competitive market environment.

• To promote GPU market diversification, Korean policymakers should provide incentives for companies to adopt AMD GPUs, such as tax breaks or subsidies for using AMD-based AI infrastructure. Government funding should also be allocated to support the development of open-source AI software and tools that are compatible with AMD GPUs, reducing reliance on NVIDIA's CUDA ecosystem. Moreover, promoting collaboration between Korean research institutions and AMD could accelerate the development of cutting-edge GPU technologies and enhance Korea's competitiveness in the global AI market.

• While NVIDIA dominates the market, AMD is actively seeking to carve out a niche with its MI300X GPU, targeting enterprise and data center deployments. However, concrete data on MI300X enterprise unit shipments in 2025 is crucial to benchmark AMD's market incursion against NVIDIA's established presence. Without precise shipment figures, assessing AMD's success in penetrating the enterprise market remains challenging.

• AMD plans to launch the MI350 series in 2025, directly challenging NVIDIA's Blackwell architecture chips (ref_idx 51). Furthermore, at the ‘Advancing AI’ event on June 12, 2025, AMD unveiled the MI350 and MI400 series chips for Helios AI servers, showcasing their commitment to open standards, unlike NVIDIA's NVLink. OpenAI, Meta, Oracle, and xAI have expressed interest in adopting the MI series, signifying a potential shift in the competitive landscape (ref_idx 282).

• Despite these efforts, NVIDIA maintains a performance edge in certain workloads. DeepSeek-V3, an AI model trained on NVIDIA H800 GPUs, has achieved performance comparable to OpenAI's commercial models, demonstrating the effectiveness of NVIDIA's hardware and software optimization (ref_idx 379). AMD admits its Instinct MI300X AI accelerator still can’t quite beat NVIDIA's H100 Hopper (ref_idx 386).

• To effectively compete with NVIDIA, AMD must not only increase its GPU shipments but also demonstrate superior performance in key AI workloads. This requires continuous investment in hardware and software optimization, as well as strategic partnerships with cloud providers and enterprises. Additionally, AMD needs to address concerns regarding its ROCm software ecosystem to attract more developers and ensure compatibility with a wider range of AI frameworks.

• AMD should focus on securing key design wins with hyperscalers and enterprises, highlighting the MI300X's advantages in specific AI workloads. AMD should also enhance its ROCm software platform, providing developers with user-friendly tools and comprehensive documentation to facilitate AI model deployment. Moreover, AMD should actively participate in industry benchmarks to showcase the MI300X's performance and demonstrate its competitiveness against NVIDIA's offerings.

• 6-2. Warranty and Ecosystem Risks

• This subsection analyzes the risks associated with GPU vendor selection, focusing on warranty expenses and the maturity of software ecosystems. It builds on the previous section's competitive analysis by examining the trade-offs between NVIDIA's established CUDA platform and AMD's ROCm, providing insights into potential vulnerabilities and long-term strategic considerations for AI infrastructure investments.

• Analyzing warranty expenses provides insights into the reliability and quality of GPUs, although limited data is available. While dozens of Taiwanese, Chinese, and American GPU manufacturers exist, only NVIDIA and AMD publicly report their warranty expenses (ref_idx 48). This lack of transparency from other manufacturers makes a comprehensive industry-wide analysis challenging.

• NVIDIA, holding approximately 80% of the discrete GPU market, specializes in GPUs for AI, machine learning, and PC gaming. Interestingly, NVIDIA voids warranties for GPUs used in cryptocurrency mining, classifying it as commercial usage exceeding normal wear and tear. This policy suggests NVIDIA acknowledges the increased stress and potential for failure under such intensive workloads (ref_idx 48).

• The limited availability of warranty data for GPUs restricts a complete assessment of the industry's overall warranty expenses. Korean companies should consider the warranty terms and conditions offered by different GPU vendors when making procurement decisions. Further investigation into failure rates and warranty claim experiences for specific GPU models may be warranted to mitigate potential risks.

• To mitigate warranty-related risks, Korean companies should diversify their GPU vendors and negotiate favorable warranty terms with suppliers. Establishing robust testing and monitoring procedures can help identify potential hardware failures early on, minimizing downtime and associated costs. Furthermore, participating in industry forums and sharing experiences with other organizations can provide valuable insights into GPU reliability and warranty support.

• Korean policymakers should encourage greater transparency in warranty reporting among GPU manufacturers. Implementing standardized warranty reporting requirements could provide valuable data for assessing GPU reliability and inform procurement decisions. Moreover, supporting research and development efforts focused on improving GPU durability and longevity could enhance the overall reliability of AI infrastructure.

• NVIDIA's CUDA platform holds a significant advantage due to its established ecosystem and wide adoption, particularly in large language model (LLM) training (ref_idx 511, 512). CUDA's early launch in 2006 and strategic promotion to universities and research labs solidified its position as the default GPU programming software. This head start has created a strong network effect, with more tools and libraries built for CUDA, making NVIDIA GPUs increasingly attractive (ref_idx 512).

• AMD's ROCm, launched approximately 10 years after CUDA, aims to offer an open-source alternative but still trails CUDA in hardware support, documentation, and ease of use. While ROCm continues to improve, CUDA benefits from a collection of AI-specific libraries and tools (CUDA X) that enhance GPU performance for AI tasks (ref_idx 511, 514). However, ROCm's open-source nature is attracting partners and customers who value flexibility and avoiding vendor lock-in (ref_idx 516).

• Quantifying developer community engagement via GitHub activity and determining enterprise ROCm deployment scale are crucial for assessing ROCm's maturity and ecosystem risk. AMD is actively seeking to reduce CUDA dependency by expanding the ROCm ecosystem and offering developer cloud services (ref_idx 509, 516). Recent versions of ROCm, such as ROCm 7, focus on supporting the latest AI models, optimizing MI350 series GPUs, and enhancing distributed resource management (ref_idx 509).

• To mitigate ecosystem risks, Korean companies should carefully evaluate the maturity and support available for different software platforms when selecting GPUs. Investing in training and development programs to build expertise in ROCm and other open-source alternatives can help reduce reliance on CUDA. Collaborating with AMD and other organizations to contribute to the ROCm ecosystem can accelerate its development and improve its competitiveness.

• Korean policymakers should support initiatives that promote open-source AI software development and adoption. Providing funding for research and development of ROCm-compatible tools and libraries can help level the playing field and reduce reliance on proprietary platforms. Encouraging collaboration between Korean research institutions and AMD could accelerate the development of cutting-edge GPU technologies and enhance Korea's competitiveness in the global AI market (ref_idx 515).

7. Storage and Network Evolution: Ultra Ethernet 1.0 and In-Network Acceleration

• 7-1. Storage System Scalability

• This subsection delves into the critical aspect of storage scalability in AI data centers, focusing on high-density solutions exemplified by VAST Data and the performance trade-offs between NVMe and HDD technologies. It bridges the previous discussion on AI infrastructure fundamentals with the subsequent analysis of network architecture innovations, highlighting how storage advancements enable efficient AI workload processing.

• Modern AI workloads demand massive storage capacities to accommodate growing datasets. VAST Data addresses this challenge with its Universal Storage platform, achieving petabyte-scale density within a single rack unit. This high density significantly reduces the data center footprint and associated costs, crucial for organizations scaling their AI initiatives.

• VAST Data's Disaggregated Shared Everything (DASE) architecture enables this density by decoupling CPUs and storage media, optimizing power utilization and maximizing flash capacity. Traditional storage architectures often struggle to efficiently utilize high-density SSDs, leading to stranded capacity and performance bottlenecks. DASE overcomes these limitations by providing a shared resource pool that can be dynamically allocated to AI workloads.

• According to ref_idx 82, VAST Data doubles data center density to feature over a petabyte of effective capacity per rack unit using Intel's 30TB QLC SSDs. Ref_idx 40 confirms that VAST Data's Flash Enclosures serve as a proven building block for AI infrastructure with DGX A100 systems. This combination ensures that AI workloads can seamlessly scale storage capacity to exabytes and performance linearly to TB/s+, meeting the evolving needs of AI applications.

• The implication is clear: Korean AI initiatives need to focus on solutions that optimize density. By adopting solutions like VAST Data's Universal Storage, Korean organizations can reduce TCO, improve power efficiency, and enable the seamless scaling of their AI infrastructure. Furthermore, adopting DASE architectures allows organizations to take advantage of cutting-edge hyperscale hardware, unlocking greater physical density and unprecedented flash capacity per total cost of acquisition.

• Korean data centers should prioritize all-flash solutions leveraging QLC SSDs and DASE architectures. Investing in technologies that maximize storage density per rack unit is crucial for achieving cost-effectiveness and sustainability in AI infrastructure deployments. Active monitoring and lifecycle management will allow the flexibility to maximize the ROI from dense solutions.

• AI workloads are highly sensitive to storage latency, with low latency being critical for training and inference tasks. NVMe SSDs offer significantly lower latency compared to traditional HDDs, enabling faster data access and improved AI performance. However, the higher cost per gigabyte of NVMe SSDs necessitates a careful evaluation of the performance benefits against the cost implications.

• NVMe SSDs leverage parallel, low-latency data paths, handling demanding workloads with a smaller infrastructure footprint compared to SAS and SATA SSDs (ref_idx 176). By using fewer storage devices and CPU cycles to achieve the same result, enterprises maximize their return on storage infrastructure. While HDDs offer consistent throughput for sequential workloads, their mechanical nature results in higher latency and lower IOPS, making them less suitable for latency-sensitive AI tasks (ref_idx 177).

• Solidigm's D7-PS1010 and D7-PS1030 NVMe SSDs are designed to overcome HDD performance limitations in AI data pipelines, offering up to 50% higher throughput in specific pipeline stages (ref_idx 170). As indicated in ref_idx 173, flash access latency is 100µs, making milliseconds of network latency unacceptable, and NVMe is even more demanding. Tight network tail latency is a requirement because storage access touches multiple devices, and the overall latency for any single storage operation is dictated by the latency of the longest network operation.

• This latency difference is critical for Korean AI initiatives. The use of NVMe SSDs can significantly reduce training times and improve inference performance, leading to faster time-to-market for AI-powered products and services. But the higher cost compared to HDDs needs to be justified through careful workload analysis and tiering strategies to optimize cost-effectiveness.

• Korean organizations should adopt a tiered storage approach, leveraging NVMe SSDs for latency-sensitive AI workloads and HDDs for less demanding tasks. Prioritizing NVMe-oF (NVMe over Fabrics) can create more efficient and faster storage systems. Savings are achieved through better performance per device, which can reduce the total number of devices needed and lower maintenance and energy costs. Detailed performance benchmarks are essential to quantify the benefits of NVMe SSDs for specific AI applications and to guide storage tiering decisions.

• 7-2. Network Architecture Innovations

• This subsection builds upon the previous discussion of storage scalability, transitioning into an exploration of cutting-edge network architecture innovations essential for AI data centers. It focuses on the growing importance of 400G+ Ethernet and In-Network Acceleration, highlighting how these advancements address the bandwidth and latency challenges posed by demanding AI workloads. It sets the stage for subsequent discussions on energy efficiency and scenario planning by outlining the fundamental network requirements for future AI infrastructure.

• AI workloads, characterized by massive data transfers and stringent latency requirements, necessitate high-bandwidth network solutions. Traditional Ethernet architectures are increasingly challenged to keep pace with the escalating demands of distributed AI training and inference. The adoption of 400G+ Ethernet is becoming crucial for overcoming these bottlenecks and enabling seamless data flow within AI data centers.

• 400G Ethernet offers significant improvements in bandwidth density, port density, and power efficiency compared to its predecessors. This increased capacity allows for the aggregation of multiple high-speed connections, reducing network complexity and improving overall performance. Moreover, advanced features such as congestion management and quality of service (QoS) enable prioritization of critical AI traffic, ensuring low latency and reliable data delivery.

• According to ref_idx 300, AMD is advocating for Ultra Ethernet Consortium (UEC) 1.0 to support million-GPU connections to achieve the hyperscale AI infrastructure expansions. While the collected documents don't explicitly provide power-per-port specifications for Ultra Ethernet 1.0, the standard's focus on efficiency suggests a drive to minimize power consumption relative to bandwidth. Cisco and NVIDIA are actively collaborating to enhance AI networking with features like Intelligent Packet Flow, optimizing performance across AI fabrics through real-time telemetry and congestion awareness (ref_idx 416).

• The shift to 400G+ Ethernet presents significant opportunities for Korean AI initiatives. By investing in high-bandwidth networking infrastructure, Korean organizations can unlock the full potential of their AI deployments, accelerate training times, and improve inference performance. Furthermore, embracing open standards like Ultra Ethernet can foster interoperability and reduce vendor lock-in.

• Korean data centers should prioritize the deployment of 400G+ Ethernet switches and network interface cards (NICs). Continuous monitoring and optimization of network performance are essential to ensure that AI workloads receive the necessary bandwidth and low latency. Active participation in industry consortia like the Ultra Ethernet Consortium can also help shape the future of AI networking standards and ensure that Korean interests are represented.

• In-Network Acceleration represents a paradigm shift in network architecture, moving data processing closer to the network fabric. This approach reduces the burden on end-servers, improves overall system performance, and lowers latency for critical AI workloads. Cisco and NVIDIA are at the forefront of this innovation, developing solutions that integrate advanced processing capabilities directly into network switches and NICs.

• Cisco-NVIDIA In-Network Acceleration leverages programmable data planes and specialized hardware to perform tasks such as data aggregation, filtering, and transformation within the network itself. This offloads these computationally intensive tasks from the CPUs and GPUs, freeing up valuable resources for AI model training and inference. Furthermore, In-Network Acceleration can optimize data flow by intelligently routing traffic based on real-time network conditions.

• Cisco's AI Job Monitoring capabilities provide comprehensive visibility and topology-aware correlation across AI jobs, networks, and GPUs (ref_idx 408). Though the documents don't explicitly state throughput figures for Cisco-NVIDIA in-network acceleration, the collaboration aims at high-throughput and low-latency connectivity. VAST Data's integration with Cisco Nexus HyperFabric highlights the focus on efficient, high-speed data access for AI applications (ref_idx 410).

• For Korean AI initiatives, adopting In-Network Acceleration can lead to significant performance gains and cost savings. By offloading data processing tasks to the network, Korean organizations can optimize the utilization of their compute resources and reduce the need for expensive server upgrades. Moreover, In-Network Acceleration can improve the security of AI deployments by enabling real-time threat detection and mitigation within the network fabric.

• Korean data centers should explore the integration of In-Network Acceleration solutions from vendors like Cisco and NVIDIA. Rigorous testing and validation are crucial to ensure that these solutions are compatible with existing AI workloads and infrastructure. Collaborating with industry partners and participating in open-source initiatives can also help accelerate the adoption of In-Network Acceleration and drive further innovation in this space.

• Ultra Ethernet 1.0 is the latest initiative by the Ultra Ethernet Consortium to deliver an Ethernet-based communication stack tailored for AI and HPC. While this report doesn't have access to power-per-port specifications, the standard aims to achieve end-to-end scalability, low latency, and interoperability (ref_idx 296, 297, 302). The official release of UEC Specification 1.0 marks a big step towards interoperable, high-performance Ethernet innovation.

• UEC 1.0 introduces advances, such as RDMA over Ethernet and IP, congestion control, and transport optimized for high-throughput, low-latency computing. This specification should allow scalability to millions of endpoints and includes provisioning and testing guidelines. As an open standard, it allows multi-vendor hardware integration, a critical hyperscale and enterprise requirement (ref_idx 296).

• Forrest Norrod of AMD revealed the official release of UEC (Ultra Ethernet Consortium) 1.0 standard and the AMD Pensando Polara 400 NIC strategy at AMD Advancing AI 2025 (ref_idx 300). According to ref_idx 300, the UEC isn't just Ethernet but a transport layer that enables efficient shared memory fabric in AI data centers.

• For Korean organizations, UEC 1.0 offers a chance to enhance AI infrastructure by adopting high-performance, interoperable, and scalable Ethernet solutions. Supporting this standard will improve data transfer speeds, diminish latency, and promote efficient AI model training.

• To fully benefit from UEC 1.0, Korean data centers must track the standard’s developments and ensure that infrastructure upgrades comply with UEC specifications. Focusing on solutions maximizing density per rack unit is essential for cost-effectiveness and sustainability in AI infrastructure deployments.

8. Energy Efficiency and Green AI Strategies

• 8-1. PUE and Renewable Integration

• This subsection analyzes the energy efficiency of Korean AI data centers by examining Power Usage Effectiveness (PUE) trends and the integration of renewable energy sources for cooling. By comparing domestic benchmarks with global standards and highlighting successful renewable cooling implementations, we aim to quantify the potential for energy cost reduction and promote sustainable AI infrastructure practices in Korea.

• Power Usage Effectiveness (PUE) serves as a key metric for evaluating the energy efficiency of data centers, representing the ratio of total energy consumed to the energy delivered to IT equipment. A lower PUE indicates greater efficiency [ref_idx 46, 47]. Globally, average PUE values have been decreasing as data centers adopt more efficient technologies and practices [ref_idx 42, 72]. However, the Korean AI-DC landscape presents a mixed picture.

• In 2016, the average PUE for Korean data centers was reported at 2.54, significantly higher than the global average, indicating relatively lower energy efficiency [ref_idx 68]. More recently, certain certified green data centers in Korea have achieved significantly better PUE scores. For instance, KT's Bundang IDC is the only data center to receive a Gold certification with a PUE of 1.5x, while SK C&C's Daeduk data center and KT's Mokdong 1 IDC have received Silver certifications with a PUE of 1.6x [ref_idx 68].

• These figures highlight a performance gap between older data centers and those implementing green technologies. Factors contributing to higher PUE in some Korean facilities may include legacy infrastructure, less efficient cooling systems, and higher power densities due to intensive AI workloads. To improve the overall PUE of Korean AI data centers, transitioning to advanced cooling solutions and optimizing energy management practices are crucial [ref_idx 70, 71].

• To promote better PUE outcomes, the government could provide incentives for data centers to adopt energy-efficient technologies, such as liquid cooling and AI-powered energy management systems, while promoting stricter PUE benchmarks for new AI-DC constructions. This effort should be coupled with transparent reporting requirements and regular audits to ensure compliance and drive continuous improvement in energy efficiency [ref_idx 73].

• Integrating renewable energy sources into data center operations, particularly for cooling, is essential for reducing carbon footprint and enhancing sustainability. While comprehensive data on renewable-powered cooling for all Korean AI-DCs is limited, specific examples demonstrate the potential for effective integration [ref_idx 39, 124].

• Naver's data center 'Gak Sejong' incorporates a hybrid cooling system utilizing natural wind and energy recycling, alongside solar power generation and geothermal energy for heating and cooling [ref_idx 74, 123]. This multifaceted approach reduces reliance on conventional electricity, lowering annual power consumption by approximately 13,000 MWh and cutting carbon emissions by 6,000 tons. The 'NAMU' air conditioning system uses outside air to cool servers, significantly reducing cooling energy [ref_idx 74].

• Additionally, distributed energy zones can enable data centers to sell excess power to the central market, creating additional revenue streams while promoting energy security [ref_idx 70]. To expand such implementations, supportive policies are crucial, such as incentives for renewable energy adoption and carbon emission trading schemes, making renewable energy integration economically viable for more AI-DCs [ref_idx 70].

• Expanding these strategies will require both technological innovation and policy support. Encouraging further R&D into renewable energy integration and efficient cooling solutions, along with the establishment of clear regulatory frameworks, will help drive the broader adoption of green AI practices across Korean data centers [ref_idx 39, 124]. Financial incentives, such as tax credits or subsidies, can further accelerate the transition to renewable-powered cooling.

• 8-2. Carbon Intensity Reduction Roadmap

• This subsection addresses the roadmap for reducing carbon intensity in Korean AI data centers, aligning infrastructure development with broader climate goals. It secures Korea’s official carbon intensity reduction targets and examines grid flexibility pilot projects, building upon the previous discussion of energy efficiency and renewable integration.

• South Korea has committed to reducing greenhouse gas emissions by 40% from 2018 levels by 2030, as part of its Nationally Determined Contribution (NDC) under the Paris Agreement [ref_idx 254, 260]. This ambitious target necessitates significant decarbonization efforts across all sectors, including the rapidly growing AI data center industry. Achieving these targets will require a multi-faceted approach, including energy efficiency improvements, renewable energy integration, and carbon capture technologies.

• Given the projected surge in electricity demand from AI applications and data centers, which could account for approximately 10% of global electricity demand growth through 2030 [ref_idx 261, 263, 264], Korean AI data centers must proactively implement strategies to mitigate their carbon footprint. Simply relying on national grid decarbonization may not be sufficient, necessitating direct action by data center operators.

• While specific carbon intensity targets for AI data centers are not explicitly defined within the NDC, the overall emissions reduction goals implicitly apply to this sector. Therefore, Korean AI data centers must align their operational strategies with the national decarbonization agenda. This includes investing in renewable energy procurement, adopting advanced cooling technologies, and optimizing workload management to minimize energy consumption. Failure to do so could result in increased scrutiny and potential regulatory interventions.

• To ensure alignment with the NDC targets, the Korean government should establish clear carbon intensity benchmarks for AI data centers and provide incentives for operators to adopt low-carbon technologies. This could include tax credits for renewable energy investments, subsidies for energy-efficient equipment, and carbon pricing mechanisms to internalize the cost of emissions. Transparent reporting and regular audits will be essential to track progress and enforce compliance.

• To enhance grid stability and facilitate the integration of renewable energy sources, Korean data centers are exploring workload choreography and demand response strategies [ref_idx 64, 257, 324]. Workload choreography involves scheduling and shifting computing workloads to align with grid conditions, reducing the overall load during peak demand periods. Demand response programs incentivize data centers to reduce their electricity consumption during periods of high demand or grid stress.

• Google is piloting workload choreography in Lenoir, NC, working directly with Duke Energy to shift computing tasks based on grid needs [ref_idx 64]. This approach is particularly viable for hyperscalers that use data centers for AI training and operate on a flexible schedule. However, enterprise and cloud services that provide real-time services, such as banking and streaming, face challenges in shifting their loads across time and geography.

• In South Korea, pilot projects are underway to explore the feasibility of demand response programs and workload choreography for data centers. For example, distributed energy zones allow data centers to sell excess power to the central market, creating additional revenue streams while enhancing energy security [ref_idx 70]. Additionally, the government is promoting the adoption of AI and IoT technologies to enhance energy efficiency measures and introduce Demand Response (DR) markets [ref_idx 257].

• To accelerate the adoption of grid flexibility strategies, the Korean government should establish clear regulatory frameworks and market mechanisms that incentivize data centers to participate in demand response programs and implement workload choreography. This includes providing financial incentives, streamlining interconnection processes, and ensuring fair compensation for grid services. Furthermore, investing in advanced grid technologies, such as smart meters and real-time monitoring systems, will be essential to enable effective coordination between data centers and grid operators. This effort should be coupled with transparent reporting requirements and regular audits to ensure compliance and drive continuous improvement in energy efficiency.

9. Scenario Planning for AI Data Center Demand and Policy Impact

• 9-1. Demand Growth Scenarios

• This subsection outlines different demand growth scenarios for AI data centers in Korea, driven by factors such as government GPU procurement and private sector cloud AI service adoption. It builds upon the previous sections detailing government policies and private sector investments, providing a foundation for subsequent policy sensitivity analysis.

• Korea's AI infrastructure development hinges significantly on the pace of GPU procurement, influenced by both government initiatives and private sector investments. Establishing clear growth trajectories is crucial for scenario planning. We model three scenarios: low, medium, and high, each reflecting different levels of GPU acquisition. The low scenario assumes a conservative CAGR of 15% in GPU shipments between 2025 and 2030, reflecting potential supply chain bottlenecks or budgetary constraints. The medium scenario projects a 25% CAGR, aligned with current government targets and moderate private sector expansion. The high scenario anticipates a 35% CAGR, driven by aggressive investment and successful localization of GPU manufacturing.

• The South Korean government aims to secure 10,000 GPUs to bolster AI infrastructure (ref_idx 2). The '2025년 산업전망' 보고서 from 대신증권 indicates that AI server GPU shipments are expected to continue to increase significantly. However, real-world data suggests the actual pace could vary. For instance, factors like delays in HBM integration or slower-than-expected infrastructure buildout could lead to the lower growth rate. Conversely, increased collaboration with international GPU providers or breakthroughs in domestic manufacturing could accelerate the procurement process.

• The three GPU procurement scenarios will critically impact the expansion of domestic AI data centers. A low GPU procurement pace could result in a smaller number of AI data centers compared to what is generally expected. However, according to a report by Mordor Intelligence, the data center accelerator market is expected to grow significantly, with the Asia-Pacific region projected to have the highest CAGR. The limited supply of GPU would make it harder for Korea to meet it's potential. In contrast, a high procurement pace would allow for greater compute availability, enabling more widespread AI adoption and the deployment of more powerful AI models across industries. By 2030, the differences in total AI DC compute capacity under these scenarios will be significant, impacting Korea's competitive positioning in the global AI landscape.

• Strategic implications for the government include diversifying GPU supply sources and incentivizing domestic GPU production. For private sector players, it means carefully managing GPU resource allocation and exploring alternative computing architectures (e.g., FPGAs or ASICs) to mitigate potential bottlenecks. Close monitoring of GPU shipment data and adjustment of AI development strategies are crucial.

• To effectively navigate these scenarios, stakeholders need to establish clear monitoring frameworks for GPU shipments and data center construction progress. The government should prioritize R&D funding for alternative computing technologies and foster collaboration between domestic and international players. Enterprises must develop flexible AI deployment strategies that can adapt to varying levels of compute resource availability, balancing cloud-based and on-premise solutions.

• Cloud AI services are projected to be a major driver of AI data center demand in Korea. Analyzing cloud AI service adoption rates is critical for estimating the scaling of AI workloads. Adoption typically follows an S-curve pattern, starting with slow initial uptake, followed by rapid acceleration, and then a plateau as the market matures. The speed and extent of this adoption curve depend on factors such as enterprise awareness, cost-effectiveness, and data security concerns.

• In 2025, cloud AI services represent a critical intersection of artificial intelligence and cloud computing technologies. According to Gartner, AI workloads are expected to consume 50% of all cloud compute resources by 2029, compared to less than 10% today (ref_idx 61). This dramatic shift reflects how businesses increasingly rely on cloud platforms to deploy and scale their AI applications without significant infrastructure investments. According to a report done by MarketsandMarkets, the global AI market is projected to grow from $515.31 billion in 2023 to $2.74 trillion by 2032, with a CAGR of 36.2%, further increasing the demand for cloud based AI (ref_idx 252).

• Different adoption curves yield distinct AI data center growth. A slow adoption rate would result in underutilization of existing AI data center capacity, while a fast adoption rate could strain resources and necessitate rapid infrastructure expansion. According to a report by Canalys Newsroom, mainland China’s cloud service spend grew by 11% in Q3 2024, indicating a return to double-digit growth, partially fueled by embedding AI capabilities into cloud applications (ref_idx 361). Gartner is projecting worldwide IT spending to reach $5.61 trillion in 2025, increasing 9.8% from 2024, with software expected to grow 14.2% (ref_idx 355).

• For cloud providers, understanding these adoption dynamics is crucial for capacity planning and service pricing. Enterprises need to carefully evaluate the cost-benefit trade-offs between public cloud, private cloud, and hybrid AI deployments. From a policy perspective, incentivizing cloud AI adoption through subsidies and regulatory support can accelerate the development of the AI ecosystem.

• Recommendations include detailed surveys of enterprise cloud AI adoption plans, coupled with real-time monitoring of cloud AI service utilization rates. Cloud providers must develop flexible pricing models that accommodate different adoption levels and usage patterns. Government initiatives should focus on addressing data security concerns and promoting interoperability standards to encourage cloud AI adoption across industries.

• 9-2. Policy Sensitivity Analysis

• This subsection builds upon the demand growth scenarios outlined in the previous section by evaluating the sensitivity of AI data center development to policy changes, particularly concerning semiconductor export controls. It analyzes the potential impact of these controls on HBM supply chain diversification and foundry CAPEX, providing a deeper understanding of the risks and potential mitigation strategies.

• The availability of High Bandwidth Memory (HBM), particularly HBM3E, is critical for AI data center deployment. The concentration of HBM supply among a few key players, namely SK Hynix, Samsung, and Micron, exposes the Korean AI infrastructure roadmap to supply chain vulnerabilities, especially in the face of potential policy shocks or export restrictions. Diversifying HBM supply sources is thus paramount to mitigating these risks.

• Currently, SK Hynix leads the HBM market, with Samsung and Micron vying for increased market share (ref_idx 456). However, geopolitical factors and trade restrictions could disrupt this balance. For instance, potential export controls targeting China's access to advanced memory technologies could indirectly impact Korean manufacturers, particularly those reliant on specific manufacturing processes or technologies originating from the US. According to TrendForce, SK Hynix held a leading position in HBM3 production and is projected to capture 48% of the US$8.9 billion HBM market in 2024 (ref_idx 462). Securing alternative supply channels becomes crucial to prevent bottlenecks and ensure stable access to HBM.

• In 2025, the HBM supply landscape is evolving, with Chinese companies like CXMT making strides in HBM2E development (ref_idx 459). While still behind the leading manufacturers, these efforts could offer a degree of diversification in the long run. According to one report, CXMT is actively developing HBM2E technology, although there remains a 6 year gap between the company and other overseas memory manufacturers (ref_idx 459). Furthermore, Samsung has secured AMD as a customer for its HBM3E 12-layer products, demonstrating its competitiveness in the market (ref_idx 457, 458). The availability of alternative suppliers could mitigate reliance on a single source and provide greater flexibility in the face of export controls.

• To mitigate HBM supply chain risks, strategic recommendations include fostering partnerships with diverse HBM suppliers, incentivizing domestic HBM production capabilities, and exploring alternative memory technologies. The Korean government should actively support R&D initiatives aimed at developing indigenous HBM technologies and reducing reliance on foreign suppliers. Establishing long-term agreements with multiple suppliers can also provide a buffer against potential disruptions.

• To ensure supply chain resilience, stakeholders should establish robust monitoring systems for HBM production capacity and market dynamics. The government should implement policies that encourage diversification of HBM suppliers and promote domestic R&D in advanced memory technologies. Enterprises should actively assess their HBM needs and develop procurement strategies that mitigate potential risks.

• Foundry CAPEX trends directly influence the availability of advanced manufacturing capacity for AI chips. Trade war scenarios and export controls can significantly disrupt these investment patterns, impacting the production of critical AI components. Understanding these dynamics is essential for evaluating the resilience of Korea's AI infrastructure development.

• The trade war between the US and China has led to increased investments in domestic semiconductor manufacturing capabilities in both countries. China's efforts to achieve semiconductor self-sufficiency have spurred substantial investments in foundry infrastructure, with companies like SMIC expanding their 28nm and above capacity (ref_idx 535). However, US export controls limit China's access to advanced manufacturing equipment, hindering its ability to produce cutting-edge AI chips.

• In 2025, global foundry CAPEX is expected to remain robust, driven by demand for advanced process technologies. However, trade restrictions and geopolitical uncertainties could lead to shifts in investment priorities and regional imbalances. The 대신증권 report projects that 삼성전자 and SK하이닉스 will allocate significant portions of their CAPEX to DRAM process conversion and HBM investments (ref_idx 128). Additionally, Samsung’s P4L facility will be crucial for NAND production starting in 2025, with DRAM equipment installation beginning in mid-2025 and mass production of 1c nanometer DRAM slated for 2026 (ref_idx 463).

• For Korean stakeholders, mitigating risks associated with foundry CAPEX disruptions requires diversifying manufacturing partnerships and fostering domestic foundry capabilities. The government should incentivize investments in advanced manufacturing technologies and support the development of a resilient domestic semiconductor ecosystem. Collaborations with international partners can also provide access to diverse manufacturing capacity and mitigate the impact of trade restrictions.

• To navigate potential trade war scenarios, stakeholders need to establish clear monitoring frameworks for global foundry CAPEX trends and policy changes. The government should prioritize R&D funding for alternative manufacturing technologies and foster collaboration between domestic and international players. Enterprises must develop flexible sourcing strategies that can adapt to varying levels of manufacturing capacity and geopolitical uncertainties.

• US export controls on AI chips directly impact the availability of high-performance computing resources for Korean AI development. These controls, aimed at limiting China's access to advanced technologies, can indirectly affect Korean companies reliant on US-origin AI chips or manufacturing equipment. Evaluating the extent of this impact is crucial for formulating effective mitigation strategies.

• The US Commerce Department has implemented measures to restrict China's access to advanced AI chips, including those from NVIDIA and AMD (ref_idx 590, 600). These restrictions have prompted China to accelerate its efforts to develop domestic AI chip alternatives. The report by the Korea Institute for International Economic Policy (KIEP) highlighted a 32.5% decline in China’s imports of semiconductor manufacturing equipment, particularly in regions with advanced production capabilities (ref_idx 588). Despite increased efforts by China, domestic AI capabilities will not be enough to surpass US capabilities.

• In 2025, the landscape for AI chip availability is characterized by increased scrutiny and tighter regulations. For example, the US administration plans to rescind and modify a Biden-era rule that curbed the export of sophisticated AI to the Chinese market, demonstrating an attempt by the US government to reduce China's ability to develop advanced technology (ref_idx 596). The global implications of this is that key AI capabilities are going to be concentrated in allied countries, which will give these countries a strategic advantage in both commercial and military applications of AI.

• For Korean stakeholders, strategic recommendations include diversifying AI chip supply sources and seeking exemptions from US export controls where possible. The government should engage in diplomatic efforts to ensure that Korean companies have access to the necessary AI chips for their operations. Actively supporting R&D initiatives aimed at developing indigenous AI chip technologies can also reduce reliance on foreign suppliers.

• To effectively address export control risks, stakeholders need to closely monitor US policy changes and assess their potential impact on Korean AI development. The government should prioritize R&D funding for alternative AI chip architectures and foster collaboration between domestic and international players. Enterprises must develop flexible AI deployment strategies that can adapt to varying levels of chip availability, balancing cloud-based and on-premise solutions.

10. Strategic Recommendations for Stakeholders

• 10-1. Government Action Priorities

• This subsection synthesizes the findings from the preceding sections to formulate actionable strategic recommendations for key stakeholders, specifically focusing on government action priorities. It builds upon the analysis of GPU supply chains, energy efficiency, and future demand scenarios to provide a sequenced approach to policy implementation for maximum ecosystem impact.

• Korea aims to bolster its semiconductor industry by strategically deploying HBM fab subsidies. The crucial decision point revolves around the timing of these subsidies in relation to NVIDIA's GPU procurement pacing. Successfully securing HBM3E qualifications from major customers like NVIDIA in Q2 2025 (ref_idx 193, 194) hinges on proactive government support for domestic HBM manufacturers.

• The core mechanism involves aligning subsidy disbursement with key milestones in the HBM development cycle. Delaying subsidies could hinder domestic manufacturers' ability to meet stringent qualification timelines, thus perpetuating reliance on foreign GPU vendors. Conversely, front-loading subsidies can accelerate HBM production capacity and improve the negotiating position of Korean firms like Samsung and SK Hynix (ref_idx 7).

• For example, ref_idx 195 details the HBM roadmap of Samsung and SK Hynix including HBM3, HBM3e, HBM4 with specification of base die process and back-end process. Securing early HBM3E qualification results in H2 2025 (ref_idx 193) strengthens Korean firm's technology and allows further expansion in HBM4 and beyond. Conversely, delays in HBM3E qualification would create market opportunity for competitors like Micron, affecting long term market share (ref_idx 207, 205).

• The strategic implication is that the government should prioritize rapid disbursement of HBM fab subsidies, especially targeting equipment upgrades and capacity expansion for HBM3E and HBM4 production. Furthermore, the supply chain should extend beyond Samsung and SK Hynix. Simmtech's AiP substrate development (ref_idx 366) demonstrates market opportunities for smaller domestic players.

• To implement this, the government should announce specific subsidy allocations and qualification timelines by Q3 2025. This includes establishing clear metrics for subsidy eligibility, focusing on HBM performance, power efficiency, and reliability. Collaborating with industry stakeholders to streamline the qualification process with NVIDIA will ensure timely market entry.

• Balancing short-term GPU needs with long-term semiconductor self-reliance requires careful management of GPU procurement lead times. The goal is to meet immediate demand for AI computing power while fostering domestic AI chip ecosystem growth (ref_idx 7). Currently, the Korean AI development relies heavily on NVIDIA GPUs, particularly H100 (ref_idx 7, 274).

• The core trade-off involves allocating resources between foreign GPU purchases and domestic NPU development. Lengthening GPU procurement lead times from foreign vendors could incentivize domestic NPU adoption, but could also stifle AI innovation due to limited compute resources. Conversely, prioritizing foreign GPU purchases ensures immediate compute availability but may delay the development of competitive domestic alternatives (ref_idx 7, 2).

• For example, the government plans to secure 10,000 advanced GPUs (ref_idx 2, 271, 272, 275) by the end of 2025, to quickly address the AI computing shortage. However, the government also aims to increase the ratio of domestically produced semiconductors used in the upcoming AI center to 50 percent by 2030 (ref_idx 272). Simultaneously prioritizing domestic NPU development and importing foreign GPUs to satisfy the demand appears as an appropriate approach.

• The strategic implication is that the government needs to manage GPU procurement lead times to create a stable demand signal for domestic NPU developers. This involves committing to a minimum purchase volume of domestic NPUs for AI computing infrastructure projects, providing incentives for early adoption and performance optimization.

• To implement this, the government should establish a rolling forecast of GPU demand, disaggregated by performance tier and application. This forecast should be shared with domestic NPU developers, enabling them to align their product roadmaps with national AI infrastructure needs. Announcing clear GPU procurement targets, with a preference for domestic solutions where available, will further incentivize local innovation.

• Securing adequate funding for cold plate cooling R&D is crucial for addressing the increasing thermal challenges of high-density AI data centers. As GPU density increases, conventional air cooling becomes insufficient, necessitating advanced thermal management solutions like cold plate cooling (ref_idx 2).

• The core challenge involves allocating sufficient R&D funding to develop and deploy cost-effective cold plate cooling solutions. Inadequate funding could hinder the adoption of energy-efficient cooling technologies, leading to higher operating costs and increased environmental impact. Conversely, prioritizing cold plate cooling R&D can establish a competitive advantage for Korean AI data centers, attracting investments and fostering innovation in related industries (ref_idx 39).

• For example, ref_idx 2 mentions support for cold plate cooling and thermal management R&D. Ref_idx 39 describes renewable-powered cooling case studies. Combining these approaches allows the possibility of sustainable and energy-efficient AI data centers.

• The strategic implication is that the government should significantly increase R&D funding for cold plate cooling technologies, focusing on performance optimization, cost reduction, and domestic manufacturing capabilities. This includes supporting collaborative projects between research institutions, industry partners, and data center operators.

• To implement this, the government should launch a dedicated funding program for cold plate cooling R&D, with clear performance targets and evaluation criteria. This program should encourage the development of innovative cooling designs, advanced materials, and efficient integration with renewable energy sources. Furthermore, establishing industry standards for cold plate cooling performance and interoperability will facilitate wider adoption and accelerate market growth.

• 10-2. Enterprise AI-DC Deployment Playbook

• This subsection transitions from government-led initiatives to enterprise-level strategies, providing a practical deployment playbook for AI data centers. It addresses key concerns around cost optimization and reliability risks in multi-cloud environments, offering actionable insights for enterprises seeking to maximize ROI while minimizing operational disruptions.

• Korean enterprises face a critical decision: whether to leverage colocation facilities or public cloud services for their AI workloads. Public cloud adoption is increasing rapidly, driven by the scalability and flexibility it offers (ref_idx 472, 473). However, the cost of AI inferencing in the cloud can be unexpectedly high, prompting many organizations to reassess their cloud strategies (ref_idx 60). A thorough cost-benefit analysis is essential to determine the most economically viable deployment model.

• The core mechanism hinges on understanding the trade-offs between capital expenditure (CAPEX) and operational expenditure (OPEX). Colocation requires significant upfront investment in hardware and infrastructure but offers predictable long-term costs. Public cloud, conversely, minimizes upfront CAPEX but incurs variable OPEX based on usage. Furthermore, enterprises need to factor in data transfer costs, compliance requirements, and potential vendor lock-in when evaluating cloud options (ref_idx 58).

• For example, colocation providers in Korea are actively investing in renewable energy solutions and advanced cooling methods such as liquid and AI-driven cooling to enhance efficiency and reduce operating expenses (ref_idx 476). This makes colocation an increasingly attractive option for enterprises seeking to minimize their carbon footprint and energy costs. Cloud service providers, on the other hand, are exploring tailored hardware accelerators alongside GPUs to optimize efficiency and reduce service charges (ref_idx 60).

• The strategic implication is that enterprises should develop a detailed cost model that accounts for their specific AI workload characteristics, performance requirements, and long-term growth projections. This model should incorporate both direct costs (e.g., compute, storage, networking) and indirect costs (e.g., management overhead, security, compliance). The analysis should consider different pricing models (e.g., pay-as-you-go, reserved instances) offered by cloud providers and colocation providers.

• To implement this, enterprises should conduct a pilot deployment in both colocation and public cloud environments to gather real-world performance and cost data. They should also leverage cost optimization tools and services offered by cloud providers and colocation providers to identify areas for cost reduction. Regularly reviewing and updating the cost model is crucial to ensure that the deployment strategy remains aligned with evolving business needs and market conditions.

• Efficient GPU lifecycle management is crucial for maximizing the return on investment in enterprise AI-DC deployments. GPUs represent a significant capital expenditure, and their value depreciates over time due to technological obsolescence and wear and tear (ref_idx 542). Understanding the depreciation schedule and optimizing GPU utilization are essential for minimizing costs and ensuring that resources are effectively deployed.

• The core mechanism involves establishing a robust asset management framework that tracks GPU usage, performance, and lifespan. This framework should incorporate regular performance monitoring, proactive maintenance, and timely upgrades to ensure that GPUs are operating at peak efficiency. Sparsity optimization techniques, such as pruning and growth for efficient inference and training in neural networks, can also extend the lifespan of GPUs and reduce the need for frequent upgrades (ref_idx 43).

• For example, ref_idx 542 suggests GPU lifetime as 4 years, which is a guideline for asset management. Additionally, the ICEF AI for Climate Change Mitigation Roadmap (ref_idx 43) emphasizes the importance of sparsity in deep learning to reduce computational demands, indicating software-level strategies to extend GPU lifecycles.

• The strategic implication is that enterprises should adopt a proactive approach to GPU lifecycle management, incorporating regular performance assessments, predictive maintenance, and strategic upgrades. By continuously monitoring GPU performance and identifying potential bottlenecks, enterprises can optimize resource allocation and extend the lifespan of their GPU assets.

• To implement this, enterprises should establish a GPU asset management system that tracks key metrics such as utilization rates, error rates, and performance benchmarks. This system should integrate with existing infrastructure monitoring tools to provide a holistic view of AI-DC performance. Regularly reviewing GPU utilization patterns and identifying underutilized resources can help enterprises optimize resource allocation and reduce unnecessary expenditures. Engaging with GPU vendors and exploring trade-in programs can also help enterprises minimize depreciation losses and stay at the forefront of technological advancements.

• GPU failure rates pose a significant risk to the reliability and availability of AI services in enterprise AI-DCs. High failure rates can lead to service disruptions, data loss, and increased maintenance costs. Implementing robust reliability risk mitigation strategies is crucial for minimizing downtime and ensuring business continuity.

• The core mechanism involves implementing proactive monitoring and diagnostics to identify potential GPU failures before they occur. This includes tracking key performance indicators (KPIs) such as temperature, power consumption, and error rates, and using machine learning algorithms to predict future failures. Redundancy and failover mechanisms are also essential for ensuring that AI services remain available in the event of a GPU failure.

• For example, SKT plans to implement rack power density of 44kW, which is far greater than the average 4.8kW (ref_idx 475). This is to ensure stable operation of GPU servers. However, operating at high rack power density leads to higher chance of failure. Therefore, redundancy and failover mechanism is crucial for minimizing downtime and ensuring business continuity.

• The strategic implication is that enterprises should invest in robust monitoring and diagnostics tools that provide real-time visibility into GPU health and performance. By proactively identifying and addressing potential issues, enterprises can minimize the risk of GPU failures and ensure that AI services remain available to users.

• To implement this, enterprises should establish a GPU reliability monitoring program that tracks key performance indicators and triggers alerts when anomalies are detected. This program should incorporate regular diagnostics and stress testing to identify potential weaknesses in the GPU infrastructure. Implementing redundant GPU configurations and failover mechanisms can provide additional protection against service disruptions.

11. Conclusion

• South Korea's journey towards becoming a global AI leader hinges on its ability to strategically navigate a complex landscape of technological advancements, geopolitical factors, and competitive dynamics. While NVIDIA currently dominates the GPU market, proactive government policies and strategic investments can foster the growth of domestic alternatives and diversify the supply chain. Embracing innovative technologies like Ultra Ethernet 1.0 and prioritizing energy efficiency are crucial for building a sustainable and competitive AI infrastructure.

• Ultimately, Korea's success will depend on its ability to strike a balance between short-term needs and long-term goals. This requires carefully managing GPU procurement, fostering domestic innovation in semiconductor manufacturing, and implementing policies that incentivize energy-efficient data center designs. By proactively addressing these challenges and capitalizing on its strengths, Korea can solidify its position as a global AI powerhouse.

• Looking ahead, continuous monitoring of market trends, technological advancements, and policy changes will be essential for adapting to the evolving AI landscape. Furthermore, fostering collaboration between government, industry, and academia will accelerate innovation and ensure that Korea remains at the forefront of AI infrastructure development. The strategic recommendations outlined in this report provide a roadmap for stakeholders to navigate these complexities and achieve their AI ambitions.

• The future of Korea's AI infrastructure will be shaped by its ability to integrate energy-efficient solutions, secure supply chains, and promote a balanced ecosystem of international and domestic players. By prioritizing innovation, sustainability, and strategic partnerships, Korea can unlock the full potential of AI and drive economic growth and societal progress.

Source Documents

• AI-Ready 데이터센터 선택 로드맵 - Empyrion Digitalhttps://empyriondigital.com/wp-content/uploads/2025/01/TechDossier_Empyrion_Ko_FINAL.pdf
• 한국형 미스트랄 AI에 정책수석 신설…이재명 정부 AI 정책https://n.news.naver.com/mnews/article/421/0008298491
• 너도나도 외치는 ‘AIDC’, 일반 데이터센터와 뭐가 다를까https://www.ddaily.co.kr/page/view/2025040111272047367
• 너도나도 외치는 ‘AIDC’, 일반 데이터센터와 뭐가 다를까 - 디지털데일리https://m.ddaily.co.kr/page/view/2025040111272047367
• AI반도체부터 스마트팩토리까지...정부, 제조·산업 AI 전환 시동https://www.digitaltoday.co.kr/news/articleView.html?idxno=570460
• AWS, SK그룹과 울산 데이터센터 설립··· "국내 AI 혁신 지원"https://www.cio.com/article/4010371/aws-sk%ea%b7%b8%eb%a3%b9%ea%b3%bc-%ec%9a%b8%ec%82%b0-%eb%8d%b0%ec%9d%b4%ed%84%b0%ec%84%bc%ed%84%b0-%ec%84%a4%eb%a6%bd%c2%b7%c2%b7%c2%b7-ai-%ec%9c%84%ed%95%9c-%ed%81%b4%eb%9d%bc%ec%9a%b0%eb%93%9c.html
• Record US data center power usage amid AI boom, forecasts EIAhttps://www.rcrwireless.com/20250612/energy/eia-power-ai-data
• ACCELERATING A.I. WORKLOADS AT LIGHTSPEEDhttps://www.militaryexpos.com/wp-content/uploads/2021/04/VAST-Data_NVIDIA_GPU-Reference-Architecture.pdf
• Most AI Investments Will Fail, Unless…https://analyticsindiamag.com/ai-features/most-ai-investments-will-fail-unless/
• ARTIFICIAL INTELLIGENCE FOR CLIMATE CHANGE ...https://lpdd.org/wp-content/uploads/2024/05/AI-Climate-Roadmap-ICEF-Dec-1-2023.pdf
• ICEF AI for Climate Change Mitigation Roadmap (Second ...https://www.icef.go.jp/wp-content/themes/icef_new/pdf/roadmap/icef2024_roadmap_AI-Climate-Second-Edition.pdf
• Bipartisan House Task Force on Artificial Intelligencehttps://www.speaker.gov/wp-content/uploads/2024/12/AI-Task-Force-Report-FINAL.pdf
• Sustainability in the world of Generative AI - Quest Journalshttps://www.questjournals.org/jses/papers/Vol9-issue-9/09090816.pdf
• GPU Warranty Expenses:https://www.warrantyweek.com/archive/ww20231026.html
• Despite criticisms, Nvidia dominates the GPU market now more than everhttps://www.xda-developers.com/despite-criticisms-nvidia-dominates-the-gpu-market-now-more-than-ever/
• Latest report shows Nvidia's GPU market share has jumped even further ahead of the competition - PC Guidehttps://www.pcguide.com/news/latest-report-shows-nvidias-gpu-market-share-has-jumped-even-further-ahead-of-the-competition/
• What is AMD (Advanced Micro Devices) | Capital.comhttps://capital.com/en-int/learn/market-guides/trade-amd
• AMD Reportedly Sold 2x As Many RX 9000 Cards As Nvidia's RTX 5000 Lineuphttps://tech4gamers.com/amd-2x-rx-9000-cards-nvidia-rtx-5000/
• Alibaba Cloud expands in South Korea to meet rising AI demandhttps://www.techinasia.com/news/alibaba-cloud-expands-south-korea-meet-rising-ai-demand
• Navigating the rising costs of AI inferencing | InfoWorldhttps://www.infoworld.com/article/4007846/navigating-the-rising-costs-of-ai-inferencing.html
• How to Choose Cloud AI Services: A Practical Roadmap for 2025 | WillDomhttps://willdom.com/blog/cloud-ai-services/
• Enterprise AI adoption stalls as inferencing costs confound cloud customershttps://www.theregister.com/2025/06/13/cloud_costs_ai_inferencing/
• Data Centers Adapt to Local Power Needshttps://spectrum.ieee.org/dcflex-data-center-flexibility
• 통신 3사 온실가스 배출 현황 평가와 노동조합 대응 과제 제안http://hcroh.org/media/notice/2024/10/22/241022%EB%B6%80%EB%A1%9D_%ED%86%B5%EC%8B%A0_3%EC%82%AC_%EC%98%A8%EC%8B%A4%EA%B0%80%EC%8A%A4_%EB%B0%B0%EC%B6%9C_%ED%98%84%ED%99%A9_%ED%8F%89%EA%B0%80%EC%99%80_%EB%85%B8%EB%8F%99%EC%A1%B0%ED%95%A9_%EB%8C%80%EC%9D%91_%EA%B3%BC%EC%A0%9C_%EC%A0%9C%EC%95%88_%EC%B5%9C%EC%A2%85.pdf
• DC구축전략 세미나, AI 대비 투자·설계 트렌드 집중조명https://www.kharn.kr/news/article.html?no=28035
• 2024년도 에너지기술개발사업 연구개발과제기획보고서https://grant-documents.thevc.kr/200277_02.+%5B%EA%B3%B5%EA%B0%9C%EC%9A%A9%5D+2024%EB%85%84%EB%8F%84+%EC%97%90%EB%84%88%EC%A7%80%EA%B8%B0%EC%88%A0%EA%B0%9C%EB%B0%9C%EC%82%AC%EC%97%85+%EC%8B%A0%EA%B7%9C%EA%B3%BC%EC%A0%9C+%EA%B8%B0%ED%9A%8D%EB%B3%B4%EA%B3%A0%EC%84%9C_%EC%97%90%EB%84%88%EC%A7%80%EC%88%98%EC%9A%94%EA%B4%80%EB%A6%AC%ED%95%B5%EC
• SUSTAINABILITY REPORT 2024 - CyrusOnehttps://www.cyrusone.com/hubfs/Website%20Documents%202025/2024_CyrusOne_Sustainability_Report.pdf
• Energy and AI - NEThttps://iea.blob.core.windows.net/assets/b8a83930-5c77-4da7-b795-270ab6a6c272/EnergyandAI.pdf
• INTEGRATED REPORT 2023 - 상장공시시스템(KIND)https://kind.krx.co.kr/external/2024/06/26/000486/20240625001176/NAVER_Integrated_Report_2023_ver3.pdf
• VAST Data Plays the End Game with Enterprise Hard Driveshttps://www.vastdata.com/press-releases/vast-data-plays-the-end-game-with-enterprise-hard-drives
• VAST Data Universal Storage Platform | IIS Technologyhttps://www.iistech.com/vast-data-universal-storage-platform
• SK Group teams up with AWS for Korea's largest W7tr AI data center - THE INVESTORhttps://www.theinvestor.co.kr/article/10514926
• President Lee Reaffirms Commitment to AI Investment - Businesskoreahttps://www.businesskorea.co.kr/news/articleView.html?idxno=245157
• “AI데이터센터 관건은 전력 확보”…SK, ‘에너지 설루션’ 차별화 - 미주 한국일보http://www.koreatimes.com/article/20250621/1569499
• South Korea Unveils Largest AI Data Center with AWS & SK | VoIP Reviewhttps://voip.review/2025/06/19/south-korea-unveils-largest-ai-data-center-aws-sk/
• SK Group, AWS to build Korea’s largest AI data center: Reporthttps://www.rcrwireless.com/20250618/ai-infrastructure/sk-group-aws-korea
• SK, AWS to build Korea’s largest AI data center in Ulsan - The Korea Heraldhttps://www.koreaherald.com/article/10510141
• SK Group to Build AI Data Center in Ulsan in Partnership with Amazon - Businesskoreahttps://www.businesskorea.co.kr/news/articleView.html?idxno=244688
• '전력망'이 갈랐다… SK·아마존 초거대 AI데이터센터 울산에 짓는 이유 - 전자부품 전문 미디어 디일렉https://www.thelec.kr/news/articleView.html?idxno=37084
• SK-AWS, 조단위 투자해 울산에 100MW급 데이터센터 건립 : 네이트 뉴스https://news.nate.com/view/20250616n11416
• 울산, 글로벌 AI 인프라 판도 흔드나···140조 아태 최대 허브 추진https://www.iusm.co.kr/news/articleView.html?idxno=1052105
• SK·AWS, 울산에 100MW급 AI 전용 데이터센터 건립…국내 최대 규모http://www.srtimes.kr/news/articleView.html?idxno=178985
• Untitled - 지속가능경영지원센터 | K-ESGhttps://bsfesg.com/uploads/post/2025/02/48534515e9f437d37a0b784d0a53f2f4.pdf
• AI 시장의 부상 : 수출 기회의 새로운 장 - 스마트시티 종합포털https://smartcity.go.kr/wp-content/uploads/2025/03/25AI%EC%8B%9C%EC%9E%A5%EC%9D%98%EB%B6%80%EC%83%81%EC%88%98%EC%B6%9C%EA%B8%B0%ED%9A%8C%EC%9D%98%EC%83%88%EB%A1%9C%EC%9A%B4%EC%9E%A5.pdf
• 2025년 산업전망 - moneycom - 대신증권http://money2.daishin.com/PDF/Out/intranet_data/Product/ResearchCenter/Report/2024/11/51768_2025_Industry_Report_PM.pdf
• 데이터 센터 가속기 시장 규모 | Mordor Intelligencehttps://www.mordorintelligence.kr/industry-reports/data-center-accelerator-market
• 인피니언, 엔비디아와 AI 데이터센터 위한 업계 최초 800V 전력 공급 아키텍처 개발 < 기업 < FOCUS < 기사본문 - 인공지능신문https://www.aitimes.kr/news/articleView.html?idxno=35289
• 인피니언, 엔비디아와 협력해 미래 AI 서버랙을 위한 혁신적인 전력 공급 아키텍처 개발 - 뉴스와이어https://www.newswire.co.kr/newsRead.php?no=1013242
• AI 대한민국 '전력 블랙홀' RE100 넘어 CF100 어떨까?https://www.econovill.com/news/articleView.html?idxno=699939
• SK, AWS to build South Korea’s largest, multibillion-dollar AI data center - KED Globalhttp://www.kedglobal.com/tech,-media-telecom/newsView/ked202506160002
• Datacenters have a public image problem, industry confesses to The Reghttps://www.theregister.com/2025/06/05/datacenters_have_a_public_image/
• 'GPU 1만장 운용' 공모에 NHN·네이버클라우드·카카오·쿠팡 참여 - 아시아경제https://www.asiae.co.kr/article/science/2025062318221214436
• GPU 1만장 운용 공모에 네이버클라우드·카카오·NHN·쿠팡 참여https://n.news.naver.com/mnews/article/001/0015465434
• 1.5조 규모 정부 GPU 사업에 네카오·NHN 도전https://n.news.naver.com/mnews/article/011/0004500443
• 네이버·카카오·NHN·쿠팡, 1.5조원 규모 GPU 대전 진검승부 펼친다https://www.ddaily.co.kr/page/view/2025062316563612331
• Gov’t to spend $1 bn on GPU procurement for AI cloud service launch - MK빌리어드https://mkbn.mk.co.kr/news/english/11317648
• 메커니즘 속도 새로운 PCIe 5.0 SSD - Solidigmhttps://www.solidigm.com/content/dam/solidigm/en/site/products/data-center/product-briefs/ps1010-ps1030/regional-translations/korean/Solidigm-CPC-Product-Brief-KOR.pdf
• Swift: Delay is Simple and Effective for Congestion Control in ...https://2022-cs244.github.io/papers/L5-swift.pdf
• NVMe SSDs - Understanding the Benefits & Use Cases | Sandiskhttps://shop.sandisk.com/ko-kr/solutions/what-is-nvme-ssd
• Server Storage Guide: How to Choose the Best Server Storagehttps://phoenixnap.com/kb/server-storage
• Building Scalable, AI-ready IT Infrastructure - Spiceworkshttps://www.spiceworks.com/tech/artificial-intelligence/guest-article/scalable-ai-it-infrastructure/
• fio iops 성능 측정 ( 리눅스 디스크 속도 측정)https://xinet.kr/?p=4160
• 투자 유망 주식/ETF/펀드 - 대신증권http://money2.daishin.co.kr/PDF/Out/intranet_data/Product/ResearchCenter/Report/2025/04/53323_250421_retail.pdf
• PowerPoint 프레젠테이션 - moneycom - 대신증권https://money2.daishin.com/PDF/Out/intranet_data/Product/ResearchCenter/Report/2025/03/53096_250331_retail.pdf
• 2025년 - 하반기 산업전망 - moneycomhttps://money2.daishin.com/PDF/Out/intranet_data/Product/ResearchCenter/Report/2025/06/53967_2H25_Tech_colla_250619.pdf
• Micron Launches HBM Facility in Singapore as Samsung and SK hynix Expand Advanced Packaging Investments | TrendForce Newshttps://www.trendforce.com/news/2025/01/15/news-micron-launches-hbm-facility-in-singapore-as-samsung-and-sk-hynix-expand-advanced-packaging-investments/
• DIGITIMES Semiconductors: Memory chips newshttps://www.digitimes.com/topic/semiconductors/memory_chips/
• I Predict This "Magnificent Seven" Stock Will Crush Expectations | The Motley Foolhttps://www.fool.com/investing/2025/06/23/i-predict-this-magnificent-seven-stock-will-crush/
• AMD's desktop GPU market share hits all-time low despite RX 9070 launch, Nvidia extends its lead [Updated]https://www.tomshardware.com/pc-components/gpus/amds-discrete-desktop-gpu-market-share-hits-all-time-low-as-nvidia-extends-its-lead
• Electricity 2025 - NEThttps://iea.blob.core.windows.net/assets/0f028d5f-26b1-47ca-ad2a-5ca3103d070a/Electricity2025.pdf
• It is expected that the project to create an independent artificial intelligence (AI) foundation mod.. - MKhttps://www.mk.co.kr/en/society/11348108
• SGVR Cluster: A Machine Learning Research Environment – SGVR Lab, Scalable Graphics, Vision, Robotics Lab. (카이스트, 대용량 컴퓨터 그래픽스, 비젼, 로보틱스 연구실)https://sgvr.kaist.ac.kr/ml-research-environment/
• AI Statistics: 500+ Facts Driving Global Innovationhttps://www.bureauworks.com/blog/ai-statistics-500-facts-driving-global-innovation
• A GREEN ECONOMY TRANSITION PROGRAM FOR SOUTH ...https://peri.umass.edu/wp-content/uploads/joomla/images/SKorea-CleanEnergy-3-25-22.pdf
• Eurative 인터뷰 : Ambassador: EU and South Korea born to be best like-minded partners 상세보기|논문/기고 | 주벨기에 유럽연합 대한민국 대사관 겸 주북대서양조약기구 대한민국 대표부https://overseas.mofa.go.kr/be-ko/brd/m_7601/view.do?seq=1337618&srchFr=&amp;srchTo=&amp;srchWord=&amp;srchTp=&amp;multi_itm_seq=0&amp;itm_seq_1=0&amp;itm_seq_2=0&amp;company_cd=&amp;company_nm=&page=1
• South Korean Energy Security - DTIChttps://apps.dtic.mil/sti/trecms/pdf/AD1127027.pdf
• Power grid, not data centers, holds key to South Korea's AI ambitions | MLex | Specialist news and analysis on legal risk and regulationhttps://www.mlex.com/mlex/artificial-intelligence/articles/2353523/power-grid-not-data-centers-holds-key-to-south-korea-s-ai-ambitions
• 2024년 정세 평가 및 2025년 전망https://www.inss.re.kr/upload/bbs/BBSA05/202412/F20241212103528191.pdf
• Sustainable Finance Themes for 2025https://www.rbccm.com/en/story/story.page?dcr=templatedata/article/story/data/2024/12/sfg-themes-2025
• Tech Giants’ carbon emissions surge 150% from 2020 to 2023: reporthttps://www.techinasia.com/news/tech-giants-carbon-emissions-surge-150-2020-2023-report
• Only 16% of Largest Companies on Track for Net Zero Goals with Nearly Half Seeing Increased Emissions, Accenture Analysis Findshttps://newsroom.accenture.com/news/2024/only-16-of-largest-companies-on-track-for-net-zero-goals-with-nearly-half-seeing-increased-emissions-accenture-analysis-finds
• Korea secures 1.9 Trillion KRW in Supplementary Budget for ...https://www.netmanias.com/en/?m=view&id=korea_ict_news&no=16329
• South Korea plans to acquire 18,000 GPUs by mid-2026https://researchsnipers.com/south-korea-plans-to-acquire-18000-gpus-by-mid-2026/
• South Korea Plans Massive GPU Acquisition to Strengthen AI Industryhttps://www.kaohooninternational.com/technology/552630
• South Korea to secure 10,000 GPUs this year for national AI computing centre - wwwcomhttps://www.lokmattimes.com/business/south-korea-to-secure-10000-gpus-this-year-for-national-ai-computing-centre/
• AMD 주가 전망 2025-2030: 엔비디아를 넘어설 수 있을까? | EBC Financial Grouphttps://www.ebc.com/kr/forex/230934.html
• Ultra Ethernet Consortium Releases Specification 1.0https://convergedigest.com/ultra-ethernet-consortium-releases-specification-1-0/
• Storage news ticker – June 16 – Blocks and Fileshttps://blocksandfiles.com/2025/06/16/storage-news-ticker-june-16/
• "초대형 AI, 100만개 GPU 연결해야"…AMD “페산도 폴라라·UEC 1.0 공개" [AMD AAI 2025]https://www.ddaily.co.kr/page/view/2025061310351524186
• Ultra Ethernet Consortium (UEC) Launches Specification 1.0 Transforming Ethernet for AI and HPC at Scalehttps://www.prnewswire.com/news-releases/ultra-ethernet-consortium-uec-launches-specification-1-0-transforming-ethernet-for-ai-and-hpc-at-scale-302478685.html
• 알리바바, 서울 제2 데이터센터 가동…"한국 시장 지속적 투자 의지"https://www.sedaily.com/NewsView/2GU4UB9BWH/GD05
• 美·中 클라우드 공습에 국내시장 '긴장' | 서울경제https://www.sedaily.com/NewsView/2GU4VO2YSM/GD05
• SKT, ‘GPU 엔진’ 달고 AI 인프라 사업 가속https://news.mediaecon.com/article/30992
• A peak load regulation method based on charging pile aggregate load &hellip;https://patentimages.storage.googleapis.com/b7/f5/1b/fbddf6dfeb8779/CN118263902A.pdf
• South Korea building new power grid to meet AI-led demand growth, but challenges loom | MLex | Specialist news and analysis on legal risk and regulationhttps://www.mlex.com/mlex/artificial-intelligence/articles/2351867/south-korea-building-new-power-grid-to-meet-ai-led-demand-growth-but-challenges-loom
• 데이터 센터 냉각 시장 규모(2024-2032년), 글로벌 보고서https://www.gminsights.com/ko/industry-analysis/data-center-cooling-market
• LG전자, 2024 Investor Forum 공개합니다 - LG전자 뉴스룸https://live.lge.co.kr/2408-lg-investor-forum/
• Zacks Industry Outlook Highlights Intuit, Adobe and Pegasystems — TradingView Newshttps://www.tradingview.com/news/zacks:a7b9afb66094b:0-zacks-industry-outlook-highlights-intuit-adobe-and-pegasystems/
• Canalys Newsroom - Mainland China’s cloud service spend grew by 11% in Q3 2024https://www.canalys.com/newsroom/china-cloud-market-q3-2024
• 사 업 보 고 서 - 심텍https://www.simmtech.com/upload/media/image/HC_20250318_01.pdf
• 박승규 / NVIDIA H100 vs H800 성능 비교 분석 | 커리어리https://careerly.co.kr/comments/116564
• AMD admits its Instinct MI300X AI accelerator still can't quite beat Nvidia's H100 Hopperhttps://www.techspot.com/news/104505-amd-admits-instinct-mi300x-ai-accelerator-cant-beat.html
• AI-Ready Infrastructure: A New Era of Data Center Designhttps://blogs.cisco.com/datacenter/ai-ready-infrastructure-a-new-era-of-data-center-design
• VAST Data selected as First Data Platform for Cisco Nexus HyperFabric – Intelligent CIO North Americahttps://www.intelligentcio.com/north-america/2024/06/05/vast-data-selected-as-first-data-platform-for-cisco-nexus-hyperfabric/
• Cisco and Nvidia Advance Security for Enterprise AI Factorieshttps://www.telecomtv.com/content/digital-platforms-services/cisco-and-nvidia-advance-security-for-enterprise-ai-factories-53192/
• Cisco and NVIDIA Advance Security for Enterprise AI Factorieshttps://blogs.nvidia.com/blog/cisco-nvidia-security-enterprise-ai/
• 눈덩이처럼 커지는 AI 추론 비용을 통제하는 실용적인 접근법 : ZUM 뉴스https://m.news.zum.com/articles/98990738/%EB%88%88%EB%8D%A9%EC%9D%B4%EC%B2%98%EB%9F%BC-%EC%BB%A4%EC%A7%80%EB%8A%94-ai-%EC%B6%94%EB%A1%A0-%EB%B9%84%EC%9A%A9%EC%9D%84-%ED%86%B5%EC%A0%9C%ED%95%98%EB%8A%94-%EC%8B%A4%EC%9A%A9%EC%A0%81%EC%9D%B8-%EC%A0%91%EA%B7%BC%EB%B2%95?
• Rakuten Mobile is happy in its private cloud bubble for nowhttps://www.fierce-network.com/cloud/rakuten-mobile-all-about-private-cloud
• Meet the trends shaping the AI year ahead - Dellhttps://www.delltechnologies.com/asset/en-gb/solutions/business-solutions/briefs-summaries/meet-the-trends-shaping-the-ai-year-ahead.pdf
• 퍼블릭 클라우드가 온프레미스 옵션을 강화하는 이유 : ZUM 뉴스https://news.zum.com/articles/98090540
• "폭등하는 AI·클라우드 비용, 하이브리드 멀티클라우드로 해결"https://zdnet.co.kr/view/?no=20250308124807
• AI Data Centres: Co-design for Trillion-Parameter Language Model Scalability.https://quantumzeitgeist.com/ai-data-centres-co-design-for-trillion-parameter-language-model-scalability/
• AMD is ‘Su’ Ready for AIhttps://analyticsindiamag.com/global-tech/amd-is-su-ready-for-ai/
• 이재용 회장 새 슈퍼컴퓨터 샀다...한국 1위, 세계 18위 성능https://n.news.naver.com/mnews/article/009/0005507547
• 삼성, 새 슈퍼컴 도입 … 세계 18위 성능 - 매일경제https://www.mk.co.kr/news/it/11341245
• ‘메모리 반도체 풍향계’ 마이크론, ‘HBM 꼴찌의 반란’ 숫자로 증명한다https://n.news.naver.com/mnews/article/366/0001087239
• 2024 Interim Business Report - Samsunghttps://images.samsung.com/is/content/samsung/assets/global/ir/docs/2024_3Q_Interim_Report.pdf
• 메트로신문http://company.emetro.co.kr/writerList?page=56&id=seoh@metroseoul.co.kr
• 마이크론, 5세대 HBM 양산 시작https://www.mydailybyte.com/post/%EB%A7%88%EC%9D%B4%ED%81%AC%EB%A1%A0-5%EC%84%B8%EB%8C%80-hbm
• Save Internet 뉴데일리https://biz.newdaily.co.kr/svc/article_print.html?no=2023110800100
• Three-way Contest for HBM Dominance, Uncertainties Surrounding China’s Supply Chain Involvement | TrendForce Newshttps://www.trendforce.com/news/2024/03/15/news-three-way-contest-for-hbm-dominance-uncertainties-surrounding-chinas-supply-chain-involvement/
• Samsung Electronics is supplying 12-layer high-bandwidth memory (HBM) 3E products to AMD's new artif.. - MKhttps://www.mk.co.kr/en/business/11342436
• 삼성전자, AMD에 HBM3E 12단 공급 확정… “HBM 결함 우려 씻어냈다”https://n.news.naver.com/mnews/article/366/0001085042
• 글로벌 HBM 시장 노리는 중국의 기술력…CXMT, HBM2E 개발 가속도https://www.epnc.co.kr/news/articleView.html?idxno=317298
• CMBIS Researchhttps://hk-official.cmbi.info/upload/f305fded-9a79-47e0-852a-48c57af5ec19.pdf
• Samsung Reportedly to Tape out HBM4 with 1c DRAM by Year-end | TrendForce Newshttps://www.trendforce.com/news/2024/08/19/news-samsung-reportedly-to-tape-out-hbm4-with-1c-dram-by-year-end/
• Canalys Newsroom - Worldwide cloud service spending to grow by 20% in 2024https://www.canalys.com/newsroom/worldwide-cloud-q4-2023
• Canalys Newsroom - Worldwide cloud service spending to grow by 19% in 2025https://www.canalys.com/newsroom/worldwide-cloud-service-q4-2024
• SKT, ‘GPU 엔진’ 달고 AI 인프라 사업 가속 | SK텔레콤 뉴스룸https://news.sktelecom.com/206514
• Data Center Power Market Report 2025-2034 | Data Center Power Industry to Hit USD 28.8 Billion by 2034 Driven by Modular Infrastructure Adoption and Energy-Efficient Technologieshttps://www.globenewswire.com/news-release/2025/04/15/3061491/28124/en/Data-Center-Power-Market-Report-2025-2034-Data-Center-Power-Industry-to-Hit-USD-28-8-Billion-by-2034-Driven-by-Modular-Infrastructure-Adoption-and-Energy-Efficient-Technologies.html
• “극단적인 파일 저장 요건”에 어울리는 HPE 파일 스토리지https://h.eventservice.kr/2024/hpe/00/0227_etevers_storage_seminar/file/Session3_Park_jaehyun.pdf
• Data Statistics (2025) - How much data is there in the world?https://rivery.io/blog/big-data-statistics-how-much-data-is-there-in-the-world/
• We’ve Just Discovered a New Way to Store Datahttps://www.franksworld.com/2025/05/06/weve-just-discovered-a-new-way-to-store-data/
• Data Storage and Analytics Trends and Predictions 2025 From Industry Insidershttps://www.itprotoday.com/data-storage/data-storage-and-analytics-trends-and-predictions-2025-from-industry-insiders
• 금융권수장인선 내부장벽 없앤다 - 지면보기 - 경제신문 이투데이https://pdf.etoday.co.kr/daily/pdf/detoday03279.pdf
• AMD, ROCm 생태계 강화로 엔비디아 도전…AI 데이터센터 시장 공략 - 서울이코노미뉴스http://www.seouleconews.com/news/articleView.html?idxno=84290
• Better artificial intelligence stock: Nvidia vs. AMDhttps://www.fool.com.au/2025/06/04/better-artificial-intelligence-stock-nvidia-vs-amd-usfeed/
• Better Artificial Intelligence Stock: Nvidia vs. AMD | The Motley Foolhttps://www.fool.com/investing/2025/06/02/better-artificial-intelligence-stock-nvidia-vs-amd/
• From Chips to Systems: How AI Is Revolutionizing ... - William Blairhttps://www.williamblair.com/-/media/downloads/eqr/2025/williamblair_how-ai-is-revolutionizing-compute-and-infrastructure.pdf
• 최신 인공지능 반도체 및 컴파일러 지원 기술 동향https://ettrends.etri.re.kr/ettrends/210/0905210013/001-011.%20%EA%B9%80%EC%9A%A9%EC%A3%BC_210%ED%98%B8%20%EC%B5%9C%EC%A2%85.pdf
• AMD’s AI Ascent: Open Source, Smart Strategy and Stable Leadership Fuel Growth – TGDailyhttps://tgdaily.com/technology/amds-ai-ascent-open-source-smart-strategy-and-stable-leadership-fuel-growth/
• 세계의 반도체 제조 장비 : 시장, 시장 점유율, 시장 예측https://www.giikorea.co.kr/report/if1473279-global-semiconductor-equipment-markets-market.html
• GAO-25-107172, ARTIFICIAL INTELLIGENCEhttps://www.gao.gov/assets/gao-25-107172.pdf
• 한국형 녹색분류체계 경제활동 해설서https://www.korearental.co.kr/taxonomy/assets/pdf/Korean_Green_Taxonomy_Economic_Activities_Explanation_Document_Final.pdf
• 분기보고서(20231114).pdf | NHNhttps://www.nhn.com/ko_/ir/AuditReport/2023/%5B%EC%97%94%EC%97%90%EC%9D%B4%EC%B9%98%EC%97%94%5D%EB%B6%84%EA%B8%B0%EB%B3%B4%EA%B3%A0%EC%84%9C(20231114).pdf
• Sustainable AIhttps://www.oreilly.com/library/view/sustainable-ai/9781098155506/ch01.html
• Samsung SDS Sustainability Report 2024https://image.samsungsds.com/us/gnb/feature/gkrf7/__icsFiles/afieldfile/2024/07/25/Samsung%20SDS%20Sustainability%20Report%202024.pdf
• AI·문화·신재생 '미래 먹거리' 투자…유망 벤처·중기 1.2조 지원[李정부 첫 추경]https://www.newsis.com/view/NISX20250618_0003217563
• 정부, 2030 국산 AI반도체 50%·인프라 자립화https://www.ddaily.co.kr/page/view/2025061810424261459
• AI 반도체 경쟁력 제고 위해 올해 798억원 지원한다http://www.itdaily.kr/news/articleView.html?idxno=233707
• 정부, 2030 국산 AI반도체 50%·인프라 자립화https://m.ddaily.co.kr/page/view/2025061810424261459
• 李 GPU 5만개 조달 공약, 산·학계 "엔비디아 종속 막고 국산 AI 칩 육성 기회로" : 네이트 뉴스https://news.nate.com/view/20250616n25442?mid=n0105
• ③ 10조 벌어 AI에 3조 쏟았다…사채 발행 과정도 간소화https://n.news.naver.com/mnews/article/293/0000068576
• 정부, AI 반도체에 2434억원 투자…국산 NPU 상용화 박차https://m.newsprime.co.kr/section_view.html?no=691912&menu=index
• 인공지능 반도체 산업 발전전략 - 중개연구 플랫폼 STEMhttps://stem.kisti.re.kr/static/docs/%EC%9D%B8%EA%B3%B5%EC%A7%80%EB%8A%A5%20%EB%B0%98%EB%8F%84%EC%B2%B4%20%EC%82%B0%EC%97%85%20%EB%B0%9C%EC%A0%84%EC%A0%84%EB%9E%B5.pdf
• 정부, 해외 GPU로 만든 AI 서비스 국산 NPU 기반 전환 지원 | 연합뉴스https://www.yna.co.kr/view/AKR20250611122700017
• 정부, 해외 GPU로 만든 AI 서비스 국산 NPU 기반 전환 지원https://stock.mk.co.kr/news/view/769119
• 정부, 국산 NPU 조기 상용화 드라이브… 추경 494억 원 투입http://www.itdaily.kr/news/articleView.html?idxno=233570
• Investigating the Effect of the U.S. Semiconductor Export Controls on China | World Economy Brief | PUBLISH : Korea Institute for International Economic Policyhttps://www.kiep.go.kr/gallery.es?mid=a20301000000&bid=0007&act=view&list_no=11473
• Nvidia will stop including China in its forecasts amid US chip export controls, CEO sayshttps://www.siliconvalley.com/2025/06/12/nvidia-will-stop-including-china-in-its-forecasts-amid-us-chip-export-controls-ceo-says/
• US claim of using Huawei AI chips ‘anywhere’ breaks its export controls a long-arm jurisdiction: Chinese expert - Global Timeshttps://www.globaltimes.cn/page/202505/1334022.shtml
• US restricts China's access to key inference chips - UBShttps://www.ubs.com/us/en/wealth-management/market-news/article/_jcr_content.0311839087.file/PS9jb250ZW50L2RhbS9pbXBvcnRlZC9jaW9yZXNlYXJjaC9wZGYvMjEvMDEvODkvNS8yMTAxODk1L2VuLzIxMDE4OTUucGRm/2101895.pdf