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Hyundai Engineering & Construction (Hyundai E&C) has officially begun the construction phase for a 40-megawatt (MW) data center in Pohang, South Korea, specifically engineered for AI workloads. Commissioned by AI Factory Pohang PFV and developed by Neo AI Cloud, the facility is designed to support high-density GPU infrastructure, including future-proofing for NVIDIA’s Vera Rubin chips. The project, which broke ground in June, is scheduled for completion in the second half of 2027, with a total construction timeline of 16.5 months.
Hyundai E&C announced on September 21 that it has finalized the main construction contract and begun installing precast concrete structures at the Pohang site. The facility will an IT load of 32MW within its 40MW total power capacity.
The data center is specifically designed for AI, with a power density approximately 10 times higher than conventional cloud data centers. It incorporates liquid-cooling technology to manage the heat generated by high-performance GPU servers.
The project is being developed by Neo AI Cloud, which intends to operate the site as an expandable platform. The company plans to offer GPU-as-a-Service (GPUaaS) and AI platform services to both domestic and international clients, specifically targeting the development of large-scale AI models.
Forrás részletei: koreaittimes.com ↗
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This project represents a significant shift in data center construction methodology, prioritizing 'Time-to-Market' to meet the rapid deployment needs of the AI industry. By utilizing precast concrete (PC) for the entire structural system—including slabs, walls, and beams—Hyundai E&C claims it can reduce structural construction time from one year to approximately two months. This approach addresses the critical bottleneck of physical infrastructure development for AI, which requires significantly higher power density and specialized cooling systems compared to traditional cloud facilities. The facility’s design, featuring a 1.25 PUE target and liquid cooling, highlights the industry's move toward managing the extreme thermal and energy demands of next-generation AI hardware.
The adoption of full-scale precast concrete construction is a notable innovation in the data center sector. By manufacturing components off-site, Hyundai E&C aims to improve construction quality and safety while drastically shortening the build cycle, which is essential for companies racing to deploy AI capacity.
The facility is explicitly designed to accommodate future hardware, such as NVIDIA’s Vera Rubin chips. This focus on 'ultra-high-density' environments reflects the industry's transition away from general-purpose cloud infrastructure toward specialized, high-performance AI environments.
The project's PUE target of 1.25 indicates a focus on energy efficiency, a critical factor given the high power consumption of AI-dedicated hardware. The success of this facility could influence future standards for AI infrastructure in South Korea.
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What should a useful AI forecast state?
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Observers should monitor the operational success of the facility's liquid-cooling systems and the effectiveness of the full-scale precast construction method in maintaining structural integrity for high-density server loads. Additionally, the project's integration with GPU-as-a-Service (GPUaaS) platforms will be a key indicator of how effectively this infrastructure supports the broader South Korean AI ecosystem. The ability of the developers to meet the 16.5-month timeline will serve as a for future AI-dedicated data center projects in the region.
The primary metric for success will be whether the project meets its operational deadline in the second half of 2027. Any delays in the assembly of the precast components or the integration of the liquid-cooling systems could impact the viability of this construction model for future projects.
Market adoption of the GPUaaS model offered by Neo AI Cloud will be a key indicator of the demand for localized, high-density AI infrastructure in South Korea.
The project's ability to maintain its 1.25 PUE target under full operational load will be a significant test for the facility's energy management strategies.