South Korea AI Infrastructure Growth: Data Centers and Computing

Introduction

Having such robust infrastructure is the backbone for the adoption of AI because their computation is tremendous. In South Korea, the growth of AI infrastructure involves public sector initiatives and investments and the latest products that would offer such capabilities. Ongoing expansion of the AI infrastructure is supporting research institutions, startups, and businesses that aim to leverage data for their strategic advantage.

Current models of AI require high performance computing infrastructure. This has created a high-level interest in investment in computing infrastructure. The existing strengths of South Korea in semiconductors/telecom infrastructure could enable it to establish an AI ready computing ecosystem. This could be done based on existing infrastructure strengths in either semiconductor/telecom infrastructure, which will enable an AI-ready computing ecosystem.

There is collaboration between public and private players that is further propelling various infrastructure initiatives. The funding programs of the public sector focus on creating national computing resources that endorse sovereign computing models and services of AI. In contrast, data center players and cloud service providers are improving their infrastructure and capacity offerings based on client demand for training and real-time inference of AI models.

One of the latest examples of product level innovation in support of this trend is from SK Telecom and involves its sovereign GPU cluster product lineup and its launch in its Gasan AI datacenter. In August of 2025, SK Telecom unveiled its GPU as a Service lineup with its NVIDIA Blackwell B200 GPUs set up in a cluster called Haein, designed for high performance computing to handle heavy-duty AI applications. This is effectively placing enterprisory-level AI computational power at the disposal of enterprises and scientists without them necessarily needing to set up heavy-duty infrastructure spending of their own for this high-value capability.

These product-related advances are accompanied by strategic infrastructure plans being made by industry players and authorities to enhance capacity, low latency, and next gen artificial intelligence capabilities. This trend of investment is instrumental in altering the dimension of artificial intelligence computing capabilities in South Korea.

To gain insights into comprehensive analysis and predictions on market growth, refer to the core report on South Korea Artificial Intelligence Market.

Primary forces and barriers

The main factor that leads to an acceleration of the development of AI infrastructure is the computational complexity of state-of-the-art AI applications. Complex models including large language models, computer vision models, and autonomous systems are demanding in terms of GPU clusters or specialized processing hardware for effective training and execution. It becomes very challenging for enterprises to pursue innovation and compete globally when infrastructure resources are limited.

There has been a particular demand for AI infrastructure solutions among organizations that have the intention to incorporate AI into their operations. Organizations in the manufacturing, finance, and health sectors require environments with computing abilities that offer robust performance and security in operation. There has been a reduction in the threshold among organizations to implement AI technology due to the availability of solutions in the form of cloud-based AI services or GPU clusters.

On top of that, government-led efforts continue to enhance infrastructure investment with plans and initiatives that promote national artificial intelligence projects and investments in research computing infrastructure. This helps minimize foreign infrastructure reliance and instead develop platforms and services that meet regional needs. However, infrastructure expansion also faces some difficulties. The problem of high capital expenditure regarding the establishment and maintenance of data centers and high-performance clusters still inhibits some of these organizations.

Power consumption and management of heating in the process of increasing computing power are also major technical difficulties encountered by these establishments. Another is the issue of the skilled labor pool. The use of AI infrastructure necessitates knowledge in cloud infrastructure, distributed computing, and systems engineering. This problem of a skills shortage can only be addressed through collective action in the education sector.

Nonetheless, infrastructure spending is projected to continue on an upward trend. With the adoption of solutions such as sovereign GPU clusters, as well as cloud-based services, expanding their offerings, a solid computing infrastructure for South Korea’s AI sector will be achieved.

Table of Contents

1. Overview of Infrastructure Needs for AI
2. Expansion of Data Centers Across Korea
3. High Performance Computing Clusters and Cloud Platforms
4. Role of Hardware Products in AI Deployment
5. Government Initiatives Supporting Infrastructure
6. Energy and Sustainability Considerations
7. Enterprise Demand for AI Compute Resources
8. Workforce and Technical Capabilities
9. Infrastructure Challenges and Opportunities

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