How to Address the Top 5 AI Data Center Infrastructure Challenges
The rush to implement AI workloads is causing a profound transformation in data center operations. Enterprises, colocation providers, and hyperscalers must substantially expand capacity while driving operational and energy efficiencies to meet sustainability objectives.
It’s a massive challenge. The data center market is projected to grow at a compound annual growth rate (CAGR) of 11.6% from 2024 to 2032. The urgency to expand forces data center operators to rethink design, deployments, and management to support the much higher-density environments that AI workloads require. The need for change touches everything from cabling to rack capacities to cooling solutions as operators face the challenges of deploying resilient, agile facilities.
Operators need solutions tailored to their needs that accelerate speed to market and comply with sustainability strategies. The most significant infrastructure challenges created by AI for data centers revolve around the following requirements:
1. Increased Power
Higher demand for power in data centers is boosting rack densities. The high-performance processors used in AI servers for model training and inference require racks with capacities reaching 100kW or more. To run these racks safely and efficiently, data centers need intelligent, high-amperage PDUs that are customizable to specific requirements and overhead track busway potentially in multiple high-amperage runs, supplying as much as 6,000A.
2. Hybrid Cooling Approaches
Thermal management in data centers traditionally has relied on air cooling methods, which cannot effectively dissipate the heat generated by AI chip clusters. This will take hybrid approaches combining air and liquid cooling. For instance, rear door heat exchangers use air-assisted liquid cooling to support racks with 150kW capacity without an infrastructure overhaul. Aisle containment also helps address the high-density challenge by ensuring consistent airflow and temperature in deployments that support both hot and cold aisles.
3. Rack Management
AI processes massive volumes of data, requiring high-density clusters that traditional racks were not designed to handle. So, as data centers rush to add capacity, operators need to deploy racks that support heavier loads and larger dimensions. Cabinets rated up to 5,000 pounds help optimize data center space. In some cases, a rack-and-stack solution may provide the ideal fit while accelerating deployment. These solutions, which handle up to 3,500 pounds, can be preloaded with all the necessary equipment for quicker deployment.
4. Cable Management and Connectivity
AI servers with graphical processing unit (GPU) clusters affect cabling requirements. Poor planning in this area can delay deployment, and inadequate cable layouts can affect airflow and cooling efficiency. Therefore, it is crucial to properly plan cabling to optimize performance, minimize latency, and reduce cable footprint. Operators can achieve all these goals with the right equipment, including optical transceivers supporting high-speed data rates of up to 800G, scalable fiber solutions, and components such as managers, panels, and enclosures that organize cables and ensure proper airflow.
5. Monitoring Operations
Higher power densities pressure data center operators to better manage power use. Real-time visibility capabilities let them monitor power at a granular level to identify and rectify power inefficiencies in AI deployments. Smart sensors and intelligent power distribution units (PDUs) capture data to help operators meet efficiency and sustainability goals. Sensors collect data on conditions such as humidity, vibration, and temperature to optimize environments, while intelligent PDUs monitor power quality to prevent data losses resulting from power quality issues.
Practical Solutions for AI Data Centers
Legrand has an extensive portfolio of easy-to-deploy, customizable solutions that can accelerate data center expansion while maximizing efficiency and scalability . Explore our practical solutions for AI data centers.