What Is a Modular Data Center (MDC)? The Next-Generation Approach to Building AI Infrastructure
Introduction: AI Data Centers Need a More Flexible Structure
As discussed in the previous article, changes in AI infrastructure are reshaping the standards for data centers.
GPU generations are changing rapidly, rack power density is increasing, and cooling methods are shifting from air cooling toward liquid cooling. In this environment, the traditional approach of completing one large facility first and then placing equipment inside is no longer sufficient to keep up with the rapidly changing requirements of AI infrastructure.
AI data centers need a structure that can be deployed faster, expanded more flexibly, and operated more efficiently.
This is why modular data centers, or MDC, are gaining attention.
What Is a Modular Data Center (MDC)?
The Core Concept: A Data Center Built by Assembling Functional Modules
A Modular Data Center (MDC) is a data center deployment model in which the facility is designed and assembled in functional module units.
Where a conventional data center is typically built as one large structure, an MDC organizes key functions such as compute rooms, UPS rooms, battery rooms, common areas, and cooling infrastructure into independent modules. These modules can then be connected or expanded depending on the required scale and purpose.
The key point is that organizations do not need to build the entire capacity from the beginning. Infrastructure can be expanded as demand grows, making MDC especially suitable for AI infrastructure environments where demand changes quickly and technology cycles are short.
MDC vs. Containerized Data Centers: Not the Same Thing
MDC is often confused with containerized data centers, but the two concepts are meaningfully different.
Containerized data centers use steel shipping containers as their structural base. They can be deployed quickly, but they have limitations in fire resistance, insulation, load capacity, and enterprise-scale expandability.
MDC, by contrast, use concrete-based modular construction to improve durability, scalability, and the ability to accommodate high-density servers. This allows MDC to maintain a level of structural stability comparable to conventional building-type data centers, while offering greater scalability and faster deployment than containerized structures.
AI data centers need to integrate many infrastructure elements from the beginning, including high-density GPU servers, high-power racks, liquid cooling, UPS systems, batteries, and coolant piping. For long-term AI infrastructure demand, a simple container-based space may not be enough.
The Advantages of Modular Data Centers
1. Faster Deployment Through a Pre-Fab Approach
MDC modules are manufactured in a factory and then assembled on-site.
This approach reduces on-site construction time, standardizes quality, and shortens the path from decision to deployment. In AI infrastructure, where fast market response is critical, deployment speed becomes a direct competitive advantage.
2. Flexible Structural Reconfiguration to Keep Pace with GPU Generation Changes
MDC allow specific functional modules to be added or replaced as needed.
If demand for GPU servers increases, additional IT room modules can be added. If power requirements rise, UPS or battery modules can be expanded. If cooling requirements change, the module configuration can be adjusted to reflect the required cooling equipment and piping structure.
This structural flexibility is especially important in AI infrastructure, where GPU generations, server architectures, and cooling requirements continue to change rapidly.
3. Structural Design for High-Density AI Servers
AI servers require far more power and cooling than general-purpose servers.
MDC can be designed from the beginning with the infrastructure required to operate high-density GPU servers, including DLC, CDU, RDHx, and BUS Way power distribution. Unlike conventional data centers designed primarily around air cooling, MDC can structurally support next-generation AI server environments.
4. Lower Initial Investment Risk and Reduced TCO Through Phased Expansion
Conventional data centers often require large-scale investment from the beginning. MDC, however, allow organizations to add modules gradually in line with demand. This can reduce the burden of initial CapEx and improve capital efficiency.
AI infrastructure is capital-intensive across GPUs, storage, networking, power systems, and cooling systems. Because MDC allow organizations to build only what they need first and expand later as demand grows, they can reduce the likelihood of idle assets and help lower overall TCO.
5. Improved Efficiency Through Integrated Operating Software
How a data center is operated matters as much as how it is built.
Low GPU utilization wastes expensive hardware. Slow fault detection affects service reliability. This makes post-deployment operation and management systems critical for AI data centers.
MDC can be combined with DCIM(Data Center Infrastructure Management), to support compute node configuration management, utilization monitoring, health checks, and integrated management of clusters and workloads.
What Makes TEN’s MDC Different?
Conventional data centers are often designed first as spaces to house servers, with equipment fitted afterward. TEN MDC takes a different approach.
The design starts with the customer’s AI workloads. Based on those workloads, the appropriate servers, storage, networking, power, cooling, and operating software are determined. In other words, TEN designs the data center around the AI infrastructure, not the other way around.
Flexible Infrastructure Configuration for Various AI Accelerators and Equipment
TEN MDC is designed on the assumption that it will operate high-performance AI infrastructure.
Internally, it supports a wide range of hardware: NVIDIA DGX H100, A100, and GPU accelerators from multiple vendors, alongside high-capacity storage and high-speed networking. The configuration is determined by the customer’s workloads, goals, budget, and growth plans.
This means TEN MDC is not a fixed structure designed for a single vendor or a specific type of equipment. Instead, the infrastructure configuration can be adjusted according to the customer’s AI workload, deployment purpose, budget, and expansion plan.
In this sense, TEN MDC is not simply a space for placing servers. It is a flexible AI infrastructure platform that allows different AI accelerators and infrastructure equipment to be combined according to each customer’s needs.
Integrated Monitoring Dashboard for Operational Visibility
Another key differentiator of TEN MDC is its operational management system.
In AI data centers, operational efficiency after deployment is extremely important. Low GPU utilization wastes expensive infrastructure, while delayed detection of equipment issues can affect service stability.
TEN’s MDC monitoring dashboard helps operators understand the overall status of the data center at a glance. From the main page, operators can quickly identify equipment that requires immediate attention, allowing them to detect issues and respond promptly when abnormalities occur.
Equipment-specific detail pages provide not only the overall infrastructure status, but also key information needed for data center management, such as power supply status, temperature, and humidity. They also display real-time graphs of server utilization, including GPU and CPU usage. This enables early fault detection and helps improve GPU utilization.
AI Infrastructure Optimization Through AI Pub and RA:X
Provisioning a data center is only part of the challenge.
Before deploying hardware, organizations need to answer harder questions: What AI models will run here? How much GPU capacity is needed? What networking and storage architecture is right? Which cooling approach fits the workloads?
TEN addresses this with two tools.
AI Pub is a software platform for AI development and operations. It focuses on maximizing infrastructure utilization across the full workload lifecycle.
RA:X is TEN's AI infrastructure consulting service. Using a customer's actual models, data samples, and performance benchmarks, RA:X delivers specific hardware and infrastructure recommendations — before any capital is committed.
Combined with TEN MDC, these capabilities change the question from "what kind of building should we construct?" to "what AI workloads do we need to run, and what's the most efficient way to run them?" That's a more valuable starting point for any infrastructure decision.
Who Is MDC Best Suited For?
MDCs are especially suitable for companies and institutions in the following situations.
Companies Building AI Model Training Infrastructure
Companies training large-scale AI models need high-performance GPU clusters, high-speed networks, and high-density cooling environments. TEN MDC can support these needs through an integrated design approach that connects the server level to the data center level.
Companies Operating AI Inference Services
Once AI services are commercialized, stable inference infrastructure becomes essential. As usage grows, the ability to expand quickly becomes especially important. Modular data centers allow capacity to be increased gradually in line with demand.
Companies That Have Secured GPUs but Lack Operating Space
Some companies pre-purchase GPUs but later find that their existing data center environment cannot accommodate them. Power, cooling, and rack density limitations can leave GPUs idle. In this situation, TEN MDC can provide an alternative through fast deployment and AI server-centered design.
Institutions Considering Their Own AI Data Centers
Public institutions, research institutions, large enterprises, and AI service companies that are considering their own AI data centers need to evaluate TCO and scalability from the initial design stage. TEN MDC can support this decision-making process through a Reference Architecture-based approach.
Conclusion: The Core of AI Data Centers Is Not Just Fast Deployment, but Flexible Evolution
GPU generations are changing quickly. Rack power density continues to rise. Cooling is shifting from air cooling to liquid cooling. In this environment, conventional fixed data center deployment models are showing clear limits in both speed and flexibility.
MDCs offer a practical alternative to these limitations. Modules can be manufactured in factories, assembled on site, expanded according to demand, and structurally adjusted to meet new AI infrastructure requirements.
For organizations serious about AI as a business capability, the data center is no longer an overhead line item. It's a competitive differentiator.
The questions that matter aren't just "which GPUs should we buy?" They extend to: Where will those GPUs be deployed? What power and cooling architecture supports them? And how do we manage total cost of ownership over the full lifecycle?
MDC is TEN's answer — a foundation for AI infrastructure that evolves as fast as the technology it runs.