{"id":225608,"date":"2024-08-09T07:00:01","date_gmt":"2024-08-09T14:00:01","guid":{"rendered":"https:\/\/zpesystems.com\/?p=225608"},"modified":"2025-04-15T12:50:23","modified_gmt":"2025-04-15T19:50:23","slug":"ai-data-center-infrastructure-zs","status":"publish","type":"post","link":"https:\/\/zpesystems.com\/ai-data-center-infrastructure-zs\/","title":{"rendered":"AI Data Center Infrastructure"},"content":{"rendered":"

[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”4.16″ da_disable_devices=”off|off|off” global_colors_info=”{}” da_is_popup=”off” da_exit_intent=”off” da_has_close=”on” da_alt_close=”off” da_dark_close=”off” da_not_modal=”on” da_is_singular=”off” da_with_loader=”off” da_has_shadow=”on”][et_pb_row _builder_version=”4.17.4″ _module_preset=”default” width=”100%” custom_margin=”0px||||false|false” custom_padding=”0px||||false|false” locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.17.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_image src=”https:\/\/zpesystems.com\/wp-content\/uploads\/2024\/08\/AI-Data-Center-Infrastructure.jpg” alt=”ZPE Systems \u2013 AI Data Center Infrastructure” title_text=”ZPE Systems \u2013 AI Data Center Infrastructure” admin_label=”Image” _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][et_pb_text _builder_version=”4.27.0″ _module_preset=”default” global_colors_info=”{}”]Artificial intelligence is transforming business operations across nearly every industry, with the recent McKinsey global survey<\/a> finding that 72% of organizations had adopted AI, and 65% regularly use generative AI (GenAI) tools specifically. GenAI and other artificial intelligence technologies are extremely resource-intensive, requiring more computational power, data storage, and energy than traditional workloads. AI data center infrastructure also requires high-speed, low-latency networking connections and unified, scalable management hardware to ensure maximum performance and availability. This post describes the key components of AI data center infrastructure before providing advice for overcoming common pitfalls to improve the efficiency of AI deployments.<\/p>\n

AI data center infrastructure components<\/h2>\n

\"A<\/p>\n

Computing<\/h3>\n

Generative AI and other artificial intelligence technologies require significant processing power. AI workloads typically run on graphics processing units (GPUs), which are made up of many smaller cores that perform simple, repetitive computing tasks in parallel. GPUs can be clustered together to process data for AI much faster than CPUs.<\/p>\n

Storage<\/h3>\n

AI requires vast amounts of data for training and inference. On-premises AI data centers typically use object storage systems with solid-state disks (SSDs) composed of multiple sections of flash memory (a.k.a., flash storage). Storage solutions for AI workloads must be modular so additional capacity can be added as data needs grow, through either physical or logical (networking) connections between devices.<\/p>\n

Networking<\/h3>\n

AI workloads are often distributed across multiple computing and storage nodes within the same data center. To prevent packet loss or delays from affecting the accuracy or performance of AI models, nodes must be connected with high-speed, low-latency networking. Additionally, high-throughput WAN connections are needed to accommodate all the data flowing in from end-users, business sites, cloud apps, IoT devices, and other sources across the enterprise.<\/p>\n

Power<\/h3>\n

AI infrastructure uses significantly more power than traditional data center infrastructure, with a rack of three or four AI servers consuming as much energy as 30 to 40<\/a> standard servers. To prevent issues, these power demands must be accounted for in the layout design for new AI data center deployments and, if necessary, discussed with the colocation provider to ensure enough power is available.<\/p>\n

Management<\/h3>\n

Data center infrastructure, especially at the scale required for AI, is typically managed with a jump box<\/a>, terminal server<\/a>, or serial console<\/a> that allows admins to control multiple devices at once. The best practice is to use an out-of-band (OOB) management<\/a> device that separates the control plane from the data plane using alternative network interfaces. An OOB console server provides several important functions:<\/p>\n

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  1. It provides an alternative path to data center infrastructure that isn\u2019t reliant on the production ISP, WAN, or LAN, ensuring remote administrators have continuous access to troubleshoot and recover systems faster, without an on-site visit.<\/li>\n
  2. It isolates management interfaces from the production network, preventing malware or compromised accounts from jumping over from an infected system and hijacking critical data center infrastructure.<\/li>\n
  3. It helps create an isolated recovery environment where teams can clean and rebuild systems during a ransomware attack or other breach without risking reinfection.<\/li>\n<\/ol>\n

    An OOB serial console helps minimize disruptions to AI infrastructure. For example, teams can use OOB to remotely control PDU outlets to power cycle a hung server. Or, if a networking device failure brings down the LAN, teams can use a 5G cellular OOB connection to troubleshoot and fix the problem. Out-of-band management reduces the need for costly, time-consuming site visits, which significantly improves the resilience<\/a> of AI infrastructure.<\/p>\n

    AI data center challenges<\/h2>\n

    Artificial intelligence workloads, and the data center infrastructure needed to support them, are highly complex. Many IT teams struggle to efficiently provision, maintain, and repair AI data center infrastructure at the scale and speed required, especially when workflows are fragmented across legacy and multi-vendor solutions that may not integrate. The best way to ensure data center teams can keep up with the demands of artificial intelligence is with a unified AI orchestration platform. Such a platform should include:<\/p>\n