{"id":37920,"date":"2025-08-25T16:08:40","date_gmt":"2025-08-25T16:08:40","guid":{"rendered":"https:\/\/serverlift.com\/?p=37920"},"modified":"2025-08-25T17:37:34","modified_gmt":"2025-08-25T17:37:34","slug":"how-data-center-gpus-have-changed-the-ai-playing-field","status":"publish","type":"post","link":"https:\/\/serverlift.com\/it\/blog\/how-data-center-gpus-have-changed-the-ai-playing-field\/","title":{"rendered":"How Data Center GPUs Have Changed The AI Playing Field"},"content":{"rendered":"<p><span style=\"font-weight: 400;\">AI is transforming how data centers operate, the functions they need to support, and even the construction of data centers. At the heart of this shift lies GPU servers.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">While CPUs (central processing units) have traditionally been used for general-purpose processing, GPU-powered infrastructure is now crucial for AI training, inference, scientific computing, and processing large datasets. Below, we\u2019ll investigate how and why GPUs are the processing solution for the AI revolution, and what this means for data centers.<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">GPUs, AI, and Data Centers: What You Need to Know<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Before we dive in, let\u2019s cover some basics.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">1. What is a GPU?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">GPU stands for Graphics Processing Unit. Originally, GPUs were designed to render video games and complex 3D graphics that were too much for the average CPU to handle.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">CPUs typically have between eight and 64 cores optimized for performing sequential computing tasks. GPUs can host thousands of smaller cores that operate in parallel to handle massive amounts of data in a shorter time period.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As technology advances, it is clear that the same parallel computing structures that made GPUs so helpful for rendering graphics also make them ideal for other intensive tasks, such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Complex mathematical computations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Scientific simulations<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Cryptography<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Financial modeling<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Video editing<\/span><\/li>\n<\/ul>\n<p><span style=\"font-weight: 400;\">GPUs are the gold standard for handling AI and deep learning tasks that traditional CPUs simply can\u2019t do.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">2. Why are GPUs now essential in data centers?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">There\u2019s a buzz-phrase circulating in tech circles these days: \u201cAI runs on GPUs.\u201d\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">GPUs are now the backbone of data center computing equipment because they easily handle tasks that would bog down a CPU.<\/span><\/p>\n<h4><strong>AI and Machine Learning<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">Training deep learning models, such as ChatGPT, Claude, and Gemini, involves billions or even trillions of calculations. CPUs running those calculations sequentially would be very slow. GPUs, on the other hand, run many computation threads at the same time and can accelerate training speeds one hundred times faster. GPUs are also ideal for inference in real-time applications such as voice assistants, chatbots, search engines, recommendation systems, and fraud detection.\u00a0<\/span><\/p>\n<h4><strong>Multitasking<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">With thousands of processing cores, GPUs can multitask on a massive scale. They can run through vector calculations, image and video processing tasks, and data transformations all at once, which is something a CPU couldn\u2019t match.\u00a0<\/span><\/p>\n<h4><strong>High Performance Computing<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">GPUs are currently the go-to for scientific and research-grade computations. Their capabilities make them ideal, for instance, in climate modeling, particle physics, and genomics.<\/span><\/p>\n<h4><strong>Data Analytics and Visualization<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">GPU-accelerated platforms such as NVIDIA RAPIDS can take traditional data workloads and boost the speed exponentially. This is incredibly helpful for big data queries and machine learning applications.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">3. GPUs vs. CPUs<\/span><\/h3>\n<table>\n<tbody>\n<tr>\n<td><b>Feature<\/b><\/td>\n<td><b>CPU<\/b><\/td>\n<td><b>GPU<\/b><\/td>\n<\/tr>\n<tr>\n<td><b>Cores<\/b><\/td>\n<td><span style=\"font-weight: 400;\">8\u201364<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Thousands (e.g., 10,000+ CUDA cores)<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Parallelism<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Sequential tasks<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Massive parallelism<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Memory<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Cache-optimized hierarchy<\/span><\/td>\n<td><span style=\"font-weight: 400;\">High-bandwidth shared memory<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>AI\/ML Performance<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Limited<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Blazing fast (esp. with Tensor Cores)<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Power Efficiency<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Lower power<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Higher throughput, higher power<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Programming<\/b><\/td>\n<td><span style=\"font-weight: 400;\">x86, C++<\/span><\/td>\n<td><span style=\"font-weight: 400;\">CUDA, OpenCL, TensorFlow, PyTorch<\/span><\/td>\n<\/tr>\n<tr>\n<td><b>Cost<\/b><\/td>\n<td><span style=\"font-weight: 400;\">Lower per chip<\/span><\/td>\n<td><span style=\"font-weight: 400;\">Higher, especially for top-tier cards<\/span><\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p><span style=\"font-weight: 400;\">GPUs excel when raw throughput and parallel computing are crucial. However, this functionality comes at the cost of much higher power and cooling demands.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">4. What are the physical characteristics of GPU servers?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">GPUs differ significantly from CPUs in terms of their physical characteristics. Consider the following when planning an equipment upgrade.<\/span><\/p>\n<h4><strong>Size and Form Factor<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">GPUs are very large. Depending on the model, a GPU can range from a single-slot to a triple-slot setup and from eight to thirteen inches (or more) in length. They typically slot into PCIe x16 connectors, although they may require risers or special chassis designs for the most efficient fit.\u00a0<\/span><\/p>\n<h4><strong>Rack Integration<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">You\u2019ll find GPUs in these server formats:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">1U to 4U GPU servers, which house anywhere from one to eight cards<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Blade servers for modular deployments<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Full GPU pods, such as NVIDIA DGX systems<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">OCP (Open Compute Project), which is commonly used by hyperscalers for custom builds<\/span><\/li>\n<\/ul>\n<h4><strong>Peso<\/strong><\/h4>\n<p><span style=\"font-weight: 400;\">Individual GPUs weigh between two and seven pounds. Fully loaded GPU servers can exceed 220 pounds (100+ kg) and require <\/span><a href=\"https:\/\/serverlift.com\/it\/data-center-lifts\/\"><span style=\"font-weight: 400;\">special lifting equipment<\/span><\/a><span style=\"font-weight: 400;\"> to move and install safely.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">5. How much power and cooling do GPUs require?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">GPUs demand a <\/span><i><span style=\"font-weight: 400;\">lot <\/span><\/i><span style=\"font-weight: 400;\">of power.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">High-performance cards, such as the NVIDIA H100, can draw 300-700W per GPU. If you run four to eight cards simultaneously, you\u2019ve reached 2,000-4,000W for a single server. It will require 208V three-phase circuits of 30-60A per rack to keep up with that kind of demand.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">As for cooling, expect a similar issue. GPUs can get extremely hot, and they do so <\/span><i><span style=\"font-weight: 400;\">quickly. <\/span><\/i><span style=\"font-weight: 400;\">Depending on density, racks can reach thermal loads of 20-40 kW or higher. They need advanced cooling techniques such as:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Liquid cooling loops<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Rear-door heat exchangers<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><span style=\"font-weight: 400;\">Hot\/cold aisle containment<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\">Airflow-optimized enclosures<\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">6. Will you need to make infrastructure adaptations to support GPUs?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Yes. Data centers need to upgrade, retrofit, or find completely new solutions for the following:<\/span><\/p>\n<ul>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Power:<\/b><span style=\"font-weight: 400;\"> You\u2019ll need high-capacity PDUs (power distribution units), redundant PSUs (power supply units), and upgraded breakers.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Cooling:<\/b><span style=\"font-weight: 400;\"> The best option is liquid cooling systems.<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Networking:<\/b><span style=\"font-weight: 400;\"> GPUs move data quickly, so you\u2019ll want InfiniBand or 100-400 Gbps Ethernet (or higher, if available).\u00a0<\/span><\/li>\n<li style=\"font-weight: 400;\" aria-level=\"1\"><b>Chassis design:<\/b><span style=\"font-weight: 400;\"> Servers will need to fit multi-GPU setups and support NVLink bridges or PCIe risers.<\/span><\/li>\n<\/ul>\n<h3><span style=\"font-weight: 400;\">7. What about GPU virtualization and multi-tenant environments?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">What if you need access to GPUs only on occasion, or you expect to work with a few GPU-powered applications? Virtual GPU (vGPU) solutions enable you to share a single GPU\u2019s compute resources with other users or split resource usage among different virtual machines within your facility. This allows more flexible, cloud-native deployments without sacrificing performance.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Popular options include NVIDIA vGPU (vComputeServer, vApps, etc.) or AMD MxGPU. Platforms such as VMware, KVM, Hyper-V, and Kubernetes also provide some support for this.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">8. What types of specialized GPU systems and cloud options are available?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">Specialized GPU systems vary depending on the use case.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For on-prem setups, NVIDIA DGX Systems excel at one-stop-shop AI training, and AMD Instinct Platforms are a great bet for open-source AI and high-performance computing.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">In the cloud, top choices include AWS EC2 P5 instances, Google Cloud TPU v4, and Azure NDv5 virtual machines. These cloud-based GPU systems are a viable option for data centers that don\u2019t want to incur the up-front investment of purchasing physical infrastructure.\u00a0<\/span><\/p>\n<h3><span style=\"font-weight: 400;\">9. What does the future hold for GPUs in data centers?<\/span><\/h3>\n<p><span style=\"font-weight: 400;\">AI isn\u2019t going to slow down anytime soon. Generative models, autonomous systems, and real-time inference are all on the rise, and that means data centers will need to support these applications in some capacity to stay competitive.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">For now, GPUs are the most versatile computing solution to handle these complex technologies. AI-specific accelerators, such as TPUs (tensor processing units) and ASICs (application-specific integrated circuits), are gaining popularity; however, they lack versatility and are not a perfect solution for every data center.\u00a0<\/span><\/p>\n<p><span style=\"font-weight: 400;\">We expect next-generation GPUs to be more integrated with storage, memory, and even CPUs, more distributed for edge AI and robotics use cases, and more energy efficient.\u00a0<\/span><\/p>\n<h2><span style=\"font-weight: 400;\">Facing a data center upgrade (or several)? Move servers with ease using dedicated server-handling devices.<\/span><\/h2>\n<p><span style=\"font-weight: 400;\">Servers and their accompanying equipment are heavy, delicate, and expensive. Don\u2019t compromise your workers\u2019 safety or risk losing your investment on damaged equipment by using subpar forklifts or warehouse lifts. Purpose-built ServerLIFT data center lifts come with safety straps for the lifted equipment, strong brakes, easy maneuvering features, and the fine-tuned lifting platform adjustments you need to get your servers exactly where they need to go, safely, with zero mishaps.\u00a0<\/span><\/p>\n<p><a href=\"https:\/\/serverlift.com\/it\/contact-us\/\"><span style=\"font-weight: 400;\">Contact ServerLIFT<\/span><\/a><span style=\"font-weight: 400;\"> and let us help you find the ideal solution for your specific needs.\u00a0<\/span><\/p>","protected":false},"excerpt":{"rendered":"<p>AI is transforming how data centers operate, the functions they need to support, and even the construction of data centers. At the heart of this shift lies GPU servers.\u00a0 While CPUs (central processing units) have traditionally been used for general-purpose processing, GPU-powered infrastructure is now crucial for AI training, inference, scientific computing, and processing large [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":37921,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":"","_wpscppro_dont_share_socialmedia":null,"_wpscppro_custom_social_share_image":0,"_facebook_share_type":"default","_twitter_share_type":"default","_linkedin_share_type":"default","_pinterest_share_type":"default","_linkedin_share_type_page":"","_instagram_share_type":"default","_medium_share_type":"default","_threads_share_type":"default","_google_business_share_type":"default","_selected_social_profile":[],"_wpsp_enable_custom_social_template":false,"_wpsp_social_scheduling":{"enabled":false,"datetime":null,"platforms":[],"status":"template_only","dateOption":"today","timeOption":"now","customDays":"","customHours":"","customDate":"","customTime":"","schedulingType":"absolute"},"_wpsp_active_default_template":true},"categories":[78],"tags":[757,756,758],"class_list":["post-37920","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","tag-data-center-gpu","tag-data-center-gpu-servers-run-ai","tag-gpu-servers-and-ai"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.7 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>GPUs Power the AI Boom in Modern Data Centers<\/title>\n<meta name=\"description\" content=\"GPUs are today\u2019s best solution for running AI applications. 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