How to Build AI Data Center: Your Complete Action Plan

Today, AI has become the core driving force behind social change. The AI data center—serving as the "digital cornerstone" of this transformation—is quietly reshaping how we process and compute data. Why is the AI data center so important? How to build AI data center?

Why Do We Need AI Data Centres?

In the era of the digital economy, AI is penetrating into various fields at an unprecedented speed. From intelligent customer service and autonomous driving to medical imaging diagnosis, the application scenarios of AI are constantly expanding. Behind these complex applications, the demands for massive data processing and model training are growing exponentially. Traditional data centers have become difficult to meet the stringent requirements of AI applications in terms of computing power, storage, and energy efficiency.​

AI data centers have emerged as the times require. They are specifically designed for AI loads and can provide powerful parallel computing capabilities to meet the needs of rapid processing of massive data in deep learning model training.

Taking the training of natural language processing models as an example, an ordinary data center may take several weeks to complete the training. However, relying on its customized hardware architecture and optimized software algorithms, an AI data center can shorten the training time to several days or even less.

At the same time, AI data centers can also achieve dynamic allocation of resources, improve resource utilization, reduce operating costs, and provide solid support for the implementation and development of AI technology.

The Core Subsystems of an AI Data Centre Infrastructure？

● Computing Subsystem

The computing subsystem leverages GPUs and TPUs. Compared with CPUs, GPUs and TPUs have strong parallel computing capabilities and can process massive amounts of data simultaneously, significantly accelerating the training and inference of deep learning models. In addition, the computing subsystem also includes high-performance servers, which are interconnected through high-speed networks to form a computing cluster to meet the computing power requirements of AI applications.

● Storage Subsystem

A distributed-storage architecture tiers hot and cold data across SSDs and HDDs: SSDs deliver low-latency access for frequently used data, while HDDs provide cost-effective capacity for infrequently accessed information. Built-in redundancy and automated backups ensure data integrity and guard against loss.

● Network Subsystem

Using InfiniBand and 25/100 GbE, the network subsystem delivers low-latency, high-bandwidth links between all components—minimizing communication delays in distributed training. An intelligent traffic-management layer dynamically allocates bandwidth to maintain service quality for critical workloads.

● Power Supply and Cooling Subsystem

In densely packed AI facilities, efficient power supply and distribution systems ensure stable and reliable electricity supply, keeping IT equipment running smoothly. Meanwhile, advanced cooling system swiftly dissipate heat, preventing localized overheating that could lead to equipment failures.

How to Build an AI Data Center: A Step-By-Step Guide

How to build a data center? The step-by-step guide is as follows:

● Requirement Analysis and Planning

Before building an AI data center, you first need to clarify the application scenarios and business requirements, estimate the data growth scale and computing power requirements in the next few years. Meanwhile, select a suitable geographical location for the data center.

● Hardware Selection and Procurement

Based on the results of the requirement analysis, select and purchase computing equipment, storage equipment, network equipment, and power supply and cooling equipment. Performance and cost need to be taken into consideration.

● Infrastructure Construction

Carry out infrastructure construction, including the construction of computer rooms with high standards, the establishment of the power supply system, reasonable cabling, and reserving space for equipment, among other tasks.

● Software System Deployment

After the hardware infrastructure is set up, the corresponding software systems need to be deployed. This includes operating systems, virtualization platforms, AI frameworks, and management software, among others.

● Testing and Optimization

After construction is completed, an AI data center must undergo comprehensive testing to verify its readiness. This process should encompass hardware stability checks, software-system compatibility tests, network-performance measurements, energy-efficiency monitoring, and stress testing under simulated real-world scenarios. Each round of testing informs targeted adjustments and optimizations—whether updating firmware, tuning software stacks, reconfiguring network topologies, or recalibrating cooling systems—to ensure the facility operates at peak efficiency and reliability.

When selecting a service provider for your AI data center, it’s wise to partner with a proven, trustworthy leader such as Huawei. With operations spanning more than 170 countries and regions and over 1,000 large-scale data-center deployments, Huawei brings unmatched expertise to the table.

Its data center facility solutions integrate advanced digital and power electronics technologies, building reliable, agile, and sustainable infrastructure. By maximizing every watt of electricity in a low-carbon, reliable, and efficient manner, Huawei offers customized solutions for hyperscale data centers as well as small- and medium-sized data centers across enterprises, banking, government, telecommunications, education, and healthcare.

Conclusion

The above is a concise guide on how to build AI data center. As the key infrastructure for AI, it combines customized hardware architectures with optimized software systems to meet strict demands for computing power, storage capacity, and energy efficiency. From deploying core subsystems to phased construction, every step is vital. Looking ahead, as AI innovations expand, AI data centers will continue to evolve—powering the growth of the digital economy.

FAQs

What does it take to build an AI data center?

The key elements for building an AI data center include: Possessing strong computing capabilities, equipped with high-performance GPU, massive storage space, and high-speed data transmission networks; Professional infrastructure such as cooling and power supply systems; Professional technical personnel for management and maintenance; A complete data security protection system, and other components.

Who builds AI data centers?

AI data-center construction is typically led by specialized facility-builders, major cloud vendors, and enterprises with deep technical expertise. For example, Huawei delivers one-stop data-center solutions to operators and businesses, enabling them to embrace intelligent computing and accelerate their digital transformation.

How is AI used in data centers?

● Intelligent operation and maintenance: Use AI technology to monitor equipment in real time and achieve proactive maintenance.

● Optimize resource allocation: Dynamically adjust resources such as servers according to business needs.

● Conduct energy optimization technology: Adjust cooling strategies in real time based on IT load and ambient temperature, provide cooling on demand, and reduce energy consumption in data centers.

How will AI data centers be powered?

AI data centers adopt a multi-layer power supply system to ensure power stability. Using the municipal power grid as the main power source, they are equipped with uninterruptible power supplies (UPS) and diesel generators to cope with sudden power outages, and are supplemented by energy storage systems such as lithium batteries to stabilize the power supply. Some data centers also introduce renewable energy sources such as solar and wind energy to save energy and reduce costs, achieving green power supply.

What are the changes of the AI data center compared with the IDC?

Compared with traditional IDC, AI data centers have major changes in the following aspects: stronger requirements for computing power, greater emphasis on high bandwidth and low latency, and stricter requirements for infrastructure reliability and energy efficiency.

What are the challenges in building AI data centers?

● Higher requirements are placed on the reliability, rapid deployment, and energy efficiency of data center infrastructure.

● Technically, it is necessary to break through the bottlenecks of hardware performance and software compatibility issues.

● The costs of construction, operation and maintenance are high.

● Professional technical talents are in short supply.

● At the same time, it is also necessary to ensure the security and compliance of data.