{"id":17695,"date":"2025-07-29T07:34:21","date_gmt":"2025-07-29T07:34:21","guid":{"rendered":"https:\/\/www.fibermall.com\/blog\/?p=17695"},"modified":"2025-08-07T10:10:44","modified_gmt":"2025-08-07T10:10:44","slug":"ai-compute-clusters-powering-the-future","status":"publish","type":"post","link":"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm","title":{"rendered":"AI Compute Clusters: Powering the Future"},"content":{"rendered":"\n<p>In recent years, the global rise of artificial intelligence (AI) has captured widespread attention across society. A common point of discussion surrounding AI is the concept of compute clusters\u2014one of the three foundational pillars of AI, alongside algorithms and data. These compute clusters serve as the primary source of computational power, akin to a massive power station continuously fueling the AI revolution.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-is-the-concept-of-compute-clusters.png\" alt=\"AI is the concept of compute clusters\" class=\"wp-image-17698\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-is-the-concept-of-compute-clusters.png 700w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-is-the-concept-of-compute-clusters-300x180.png 300w\" sizes=\"(max-width: 700px) 100vw, 700px\" \/><\/figure>\n\n\n\n<p>But what exactly constitutes an AI compute cluster? Why are they capable of delivering such immense computational performance? What does their internal architecture look like, and what key technologies are involved?<\/p>\n\n\n\n<div id=\"ez-toc-container\" class=\"ez-toc-v2_0_76 ez-toc-wrap-left counter-hierarchy ez-toc-counter ez-toc-grey ez-toc-container-direction\">\n<div class=\"ez-toc-title-container\">\n<p class=\"ez-toc-title\" style=\"cursor:inherit\">Table of Contents<\/p>\n<span class=\"ez-toc-title-toggle\"><a href=\"#\" class=\"ez-toc-pull-right ez-toc-btn ez-toc-btn-xs ez-toc-btn-default ez-toc-toggle\" aria-label=\"Toggle Table of Content\"><span class=\"ez-toc-js-icon-con\"><span class=\"\"><span class=\"eztoc-hide\" style=\"display:none;\">Toggle<\/span><span class=\"ez-toc-icon-toggle-span\"><svg style=\"fill: #999;color:#999\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" class=\"list-377408\" width=\"20px\" height=\"20px\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M6 6H4v2h2V6zm14 0H8v2h12V6zM4 11h2v2H4v-2zm16 0H8v2h12v-2zM4 16h2v2H4v-2zm16 0H8v2h12v-2z\" fill=\"currentColor\"><\/path><\/svg><svg style=\"fill: #999;color:#999\" class=\"arrow-unsorted-368013\" xmlns=\"http:\/\/www.w3.org\/2000\/svg\" width=\"10px\" height=\"10px\" viewBox=\"0 0 24 24\" version=\"1.2\" baseProfile=\"tiny\"><path d=\"M18.2 9.3l-6.2-6.3-6.2 6.3c-.2.2-.3.4-.3.7s.1.5.3.7c.2.2.4.3.7.3h11c.3 0 .5-.1.7-.3.2-.2.3-.5.3-.7s-.1-.5-.3-.7zM5.8 14.7l6.2 6.3 6.2-6.3c.2-.2.3-.5.3-.7s-.1-.5-.3-.7c-.2-.2-.4-.3-.7-.3h-11c-.3 0-.5.1-.7.3-.2.2-.3.5-.3.7s.1.5.3.7z\"\/><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n<nav><ul class='ez-toc-list ez-toc-list-level-1 ' ><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-1\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#What_Are_AI_Compute_Clusters\" >What Are AI Compute Clusters?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-2\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#What_Is_Scale_Up\" >What Is Scale Up?<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-3\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#What_Is_Scale_Out\" >What Is Scale Out?<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-4\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#Evolving_for_AI_Demands\" >Evolving for AI Demands<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-5\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#InfiniBand_vs_RoCEv2\" >InfiniBand vs RoCEv2<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-6\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#Performance_Differences_Scale_Up_vs_Scale_Out\" >Performance Differences: Scale Up vs Scale Out<\/a><ul class='ez-toc-list-level-3' ><li class='ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-7\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#Application_in_AI_Training\" >Application in AI Training<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-8\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#Advantages_of_Scale_Up_in_Network_Design\" >Advantages of Scale Up in Network Design<\/a><\/li><li class='ez-toc-page-1 ez-toc-heading-level-3'><a class=\"ez-toc-link ez-toc-heading-9\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#A_Unified_Future\" >A Unified Future?<\/a><\/li><\/ul><\/li><li class='ez-toc-page-1 ez-toc-heading-level-2'><a class=\"ez-toc-link ez-toc-heading-10\" href=\"https:\/\/www.fibermall.com\/blog\/ai-compute-clusters.htm\/#Trends_in_AI_Compute_Cluster_Development\" >Trends in AI Compute Cluster Development<\/a><\/li><\/ul><\/nav><\/div>\n<h2 class=\"wp-block-heading\" id=\"h-what-are-ai-compute-clusters\"><span class=\"ez-toc-section\" id=\"What_Are_AI_Compute_Clusters\"><\/span><strong>What Are AI Compute Clusters?<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>As the name suggests, an AI compute cluster is a system that delivers the computational resources required to perform AI tasks. A &#8220;cluster&#8221; refers to a group of independent devices connected via high-speed networks to function as a unified system.<\/p>\n\n\n\n<p>By definition, an AI compute cluster is a distributed computing system formed by interconnecting numerous high-performance computing nodes (such as GPU or TPU servers) over high-speed networks.<\/p>\n\n\n\n<p>AI workloads can generally be divided into two major categories: training and inference. Training tasks are typically more computation-intensive and complex, requiring significant computational resources. Inference tasks, by contrast, are relatively lightweight and less demanding.<\/p>\n\n\n\n<p>Both processes heavily rely on matrix operations\u2014including convolutions and tensor multiplications\u2014that naturally lend themselves to parallelization. Thus, parallel computing chips such as GPUs, NPUs, and TPUs have become essential for AI processing. Collectively, these are referred to as AI chips.<\/p>\n\n\n\n<p>AI chips are the fundamental units of AI computation, but a single chip cannot operate independently; it must be integrated into a circuit board. Depending on the application:<\/p>\n\n\n\n<p>Embedded in mobile phone motherboards or integrated into SoCs, they power mobile AI capabilities.<\/p>\n\n\n\n<p>Installed in modules for IoT devices, they enable edge intelligence for equipment such as autonomous vehicles, robotic arms, and surveillance cameras.<\/p>\n\n\n\n<p>Integrated into base stations, routers, and gateways, they provide edge-side AI computing\u2014typically limited to inference due to size and power constraints.<\/p>\n\n\n\n<p>For more demanding training tasks, systems must support multiple AI chips. This is achieved by building AI compute boards and installing several into a single server, effectively transforming a standard server into an AI server.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-server.jpg\" alt=\"AI server\" class=\"wp-image-17699\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-server.jpg 890w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-server-300x169.jpg 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/AI-server-768x433.jpg 768w\" sizes=\"(max-width: 890px) 100vw, 890px\" \/><\/figure>\n\n\n\n<p>Typically, an AI server houses eight compute cards, though some models support up to twenty cards. However, due to heat dissipation and power limitations, further expansion becomes impractical.<\/p>\n\n\n\n<p>With this configuration, the computational capability increases significantly\u2014allowing the server to easily handle inference and even perform smaller-scale training tasks. An example is the DeepSeek model, which has optimized its architecture and algorithms to significantly reduce computational demands. Consequently, many vendors now offer integrated &#8220;all-in-one&#8221; racks\u2014consisting of AI servers, storage, and power supplies\u2014that support private deployment of DeepSeek models for enterprise customers.<\/p>\n\n\n\n<p>Nevertheless, the computational power of these setups remains finite. Training extremely large-scale models\u2014those with tens or hundreds of billions of parameters\u2014demands far greater resources. This leads to the development of large-scale AI compute clusters, which incorporate an even greater number of AI chips.<\/p>\n\n\n\n<p>Terms like &#8220;10K-scale&#8221; or &#8220;100K-scale&#8221; refer to clusters comprising 10,000 or 100,000 AI compute boards. To achieve this, two fundamental strategies are employed: Scale Up (adding more powerful hardware) and Scale Out (expanding the number of interconnected systems).<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-scale-up\"><span class=\"ez-toc-section\" id=\"What_Is_Scale_Up\"><\/span><strong>What Is Scale Up?<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>In computing terminology, \u201cscale\u201d&nbsp;refers to the expansion of system resources. This concept is particularly familiar to those with experience in cloud computing.<\/p>\n\n\n\n<p>Scale Up, also known as vertical scaling, involves increasing the resources of a single node\u2014such as adding more computing power, memory, or AI accelerator cards to a server.<\/p>\n\n\n\n<p>Scale Out, or horizontal scaling, means expanding a system by adding more nodes\u2014connecting multiple servers or devices through a network.<\/p>\n\n\n\n<p>In cloud computing, the concepts also extend to Scale Down (reducing resources of a node) and Scale In (reducing the number of nodes).<\/p>\n\n\n\n<p>Earlier, we discussed how inserting more AI accelerator cards into a server is a form of Scale Up, with each server acting as a single node. By interconnecting multiple servers via high-speed networks, we achieve Scale Out.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/External-Node-Interconnection-1-1024x438.png\" alt=\"External Node Interconnection\" class=\"wp-image-17701\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/External-Node-Interconnection-1-1024x438.png 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/External-Node-Interconnection-1-300x128.png 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/External-Node-Interconnection-1-768x329.png 768w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/External-Node-Interconnection-1.png 1080w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>The main distinction between these two lies in the communication bandwidth between AI chips:<\/p>\n\n\n\n<p>Scale Up involves internal node connections, offering higher speed, lower latency, and stronger performance.<\/p>\n\n\n\n<p>Historically, internal communications within computers relied on PCIe\u2014a protocol developed in the late 20th century during the rise of personal computing. Although PCIe has undergone several upgrades, its evolution has been slow and insufficient for modern AI workloads.<\/p>\n\n\n\n<p>To overcome these limitations, NVIDIA introduced its proprietary NVLINK bus protocol in 2014, enabling point-to-point communication between GPUs. NVLINK delivers far greater speed and substantially lower latency compared to PCIe.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-nvlink-1024x576.png\" alt=\"nvidia nvlink\" class=\"wp-image-17702\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-nvlink-1024x576.png 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-nvlink-300x169.png 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-nvlink-768x432.png 768w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-nvlink.png 1080w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p>Initially used only for intra-machine communication, NVIDIA released NVSwitch in 2022\u2014an independent switching chip designed to enable high-speed GPU connectivity across servers. This innovation redefined the concept of a node, allowing multiple servers and networking devices to work collectively within a High Bandwidth Domain (HBD).<\/p>\n\n\n\n<p>NVIDIA refers to such systems, where more than 16 GPUs are interconnected with ultra-high bandwidth, as supernodes.<\/p>\n\n\n\n<p>Over time, NVLINK has progressed to its fifth generation. Each GPU now supports up to 18 NVLINK connections, and the Blackwell GPU&#8217;s total bandwidth has reached 1800 GB\/s, vastly exceeding PCIe Gen6.<\/p>\n\n\n\n<p>In March 2024, NVIDIA unveiled the NVL72, a liquid-cooled cabinet that integrates 36 Grace CPUs and 72 Blackwell GPUs. It delivers up to 720 PFLOPS of training performance or 1440 PFLOPS of inference performance, further solidifying NVIDIA\u2019s leadership in the AI computing ecosystem\u2014powered by its popular GPU hardware and the CUDA software stack.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-GB200-NVL72.png\" alt=\"nvidia GB200 NVL72\" class=\"wp-image-17703\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-GB200-NVL72.png 800w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-GB200-NVL72-300x169.png 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/nvidia-GB200-NVL72-768x432.png 768w\" sizes=\"(max-width: 800px) 100vw, 800px\" \/><\/figure>\n\n\n\n<p>As AI adoption expanded, numerous other companies developed their own AI chips. Due to NVLINK&#8217;s proprietary nature, these companies had to devise alternative compute cluster architectures.<\/p>\n\n\n\n<p>AMD, a major competitor, introduced UA LINK.<\/p>\n\n\n\n<p>Domestic players in China\u2014such as Tencent, Alibaba, and China Mobile\u2014spearheaded open initiatives like ETH-X, ALS, and OISA.<\/p>\n\n\n\n<p>Another noteworthy advancement is Huawei\u2019s UB (Unified Bus) protocol, a proprietary technology developed to support the Ascend AI chip ecosystem. Huawei\u2019s chips, such as the Ascend 910C, have evolved considerably in recent years.<\/p>\n\n\n\n<p>In April 2025, Huawei launched the CloudMatrix384 supernode, integrating 384 Ascend 910C AI accelerator cards and achieving up to 300 PFLOPS of dense BF16 compute performance\u2014nearly double that of NVIDIA&#8217;s GB200 NVL72 system.<\/p>\n\n\n\n<p>CloudMatrix384 leverages UB technology and consists of three distinct networking planes:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>UB Plane<\/li>\n\n\n\n<li>RDMA (Remote Direct Memory Access) Plane<\/li>\n\n\n\n<li>VPC (Virtual Private Cloud) Plane<\/li>\n<\/ul>\n\n\n\n<p>These complementary planes enable exceptional inter-card communication and significantly enhance overall computational power within the supernode.<\/p>\n\n\n\n<p>Due to space limitations, we\u2019ll explore the technical details of these planes separately in a future discussion.<\/p>\n\n\n\n<p>One final note: In response to the growing pressure from open standards, NVIDIA recently announced its NVLink Fusion initiative, offering access to its NVLink technology to eight global partners. This move aims to help them build customized AI systems via multi-chip interconnectivity. However, according to some media reports, key NVLink components remain proprietary, suggesting NVIDIA is still somewhat reserved in its openness.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-what-is-scale-out\"><span class=\"ez-toc-section\" id=\"What_Is_Scale_Out\"><\/span><strong>What Is Scale Out?<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>Scale Out refers to the horizontal expansion of computing systems and closely resembles traditional data communication networks. Technologies commonly used to connect conventional servers\u2014such as fat-tree architecture, spine-leaf network topology, TCP\/IP protocols, and Ethernet\u2014form the foundational basis of Scale Out infrastructure.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-full is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/bandwidth-and-latency.png\" alt=\"bandwidth and latency\" class=\"wp-image-17704\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/bandwidth-and-latency.png 789w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/bandwidth-and-latency-300x126.png 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/bandwidth-and-latency-768x322.png 768w\" sizes=\"(max-width: 789px) 100vw, 789px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-evolving-for-ai-demands\"><span class=\"ez-toc-section\" id=\"Evolving_for_AI_Demands\"><\/span><strong>Evolving for AI Demands<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>With the growing demands of AI workloads, traditional networking technologies have required substantial enhancements to meet performance criteria. Currently, the two dominant network technologies for Scale Out are:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>InfiniBand (IB)<\/li>\n\n\n\n<li>RoCEv2 (RDMA over Converged Ethernet version 2)<\/li>\n<\/ul>\n\n\n\n<p>Both are based on the RDMA (Remote Direct Memory Access) protocol, providing higher data transfer rates, lower latency, and superior load-balancing capabilities compared to traditional Ethernet.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-infiniband-vs-rocev2\"><span class=\"ez-toc-section\" id=\"InfiniBand_vs_RoCEv2\"><\/span><strong><a href=\"https:\/\/www.fibermall.com\/blog\/how-to-choose-between-infiniband-and-roce.htm\" target=\"_blank\" rel=\"noreferrer noopener\">InfiniBand vs RoCEv2<\/a><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<ul class=\"wp-block-list\">\n<li>InfiniBand was originally designed to replace PCIe for interconnect purposes. Although its adoption fluctuated over time, it was ultimately acquired by NVIDIA through its purchase of Mellanox. Today, IB is proprietary to NVIDIA and plays a key role in their compute infrastructure. While it offers excellent performance, it comes with a high price tag.<\/li>\n\n\n\n<li>RoCEv2, on the other hand, is an open standard developed to counterbalance IB\u2019s market dominance. It merges RDMA with conventional Ethernet, offering cost efficiency and steadily narrowing the performance gap with InfiniBand.<\/li>\n<\/ul>\n\n\n\n<p>Unlike the fragmented standards seen in Scale Up implementations, Scale Out is largely unified under RoCEv2, due to its emphasis on inter-node compatibility, rather than tight coupling with chip-level products.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-performance-differences-scale-up-vs-scale-out\"><span class=\"ez-toc-section\" id=\"Performance_Differences_Scale_Up_vs_Scale_Out\"><\/span><strong>Performance Differences: Scale Up vs Scale Out<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>The primary technical differences between Scale Up and Scale Out lie in bandwidth and latency:<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/infiniband-vs-Ethernet-1024x503.png\" alt=\"infiniband vs Ethernet\" class=\"wp-image-17705\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/infiniband-vs-Ethernet-1024x503.png 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/infiniband-vs-Ethernet-300x147.png 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/infiniband-vs-Ethernet-768x377.png 768w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/infiniband-vs-Ethernet.png 1080w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-application-in-ai-training\"><span class=\"ez-toc-section\" id=\"Application_in_AI_Training\"><\/span><strong>Application in AI Training<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>AI training involves multiple forms of parallel computation:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>TP (Tensor Parallelism)<\/li>\n\n\n\n<li>EP (Expert Parallelism)<\/li>\n\n\n\n<li>PP (Pipeline Parallelism)<\/li>\n\n\n\n<li>DP (Data Parallelism)<\/li>\n<\/ul>\n\n\n\n<p>Generally:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>PP and DP involve smaller communication loads and are handled via Scale Out.<\/li>\n\n\n\n<li>TP and EP, which require heavier data exchange, are best supported by Scale Up within supernodes.<\/li>\n<\/ul>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-advantages-of-scale-up-in-network-design\"><span class=\"ez-toc-section\" id=\"Advantages_of_Scale_Up_in_Network_Design\"><\/span><strong>Advantages of Scale Up in Network Design<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Supernodes\u2014built on Scale Up architecture\u2014are connected through high-speed internal buses and provide efficient support for parallel computing, GPU parameter exchange, and data synchronization. They also enable direct memory access between GPUs, a capability Scale Out lacks.<\/p>\n\n\n\n<p>From a deployment and maintenance standpoint:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Larger High Bandwidth Domains (HBDs) simplify the Scale Out network.<\/li>\n\n\n\n<li>Pre-integrated Scale Up systems reduce complexity, shorten deployment time, and ease long-term operations.<\/li>\n<\/ul>\n\n\n\n<p>However, Scale Up cannot expand infinitely due to cost constraints. The optimal scale depends on specific usage scenarios.<\/p>\n\n\n\n<figure class=\"wp-block-image aligncenter size-large is-resized\"><img decoding=\"async\" src=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/Advantages-of-Scale-Up-in-Network-Design-1024x541.png\" alt=\"Advantages of Scale Up in Network Design\" class=\"wp-image-17706\" style=\"width:800px\" width=\"800\" srcset=\"https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/Advantages-of-Scale-Up-in-Network-Design-1024x541.png 1024w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/Advantages-of-Scale-Up-in-Network-Design-300x159.png 300w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/Advantages-of-Scale-Up-in-Network-Design-768x406.png 768w, https:\/\/www.fibermall.com\/blog\/wp-content\/uploads\/2025\/07\/Advantages-of-Scale-Up-in-Network-Design.png 1099w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"h-a-unified-future\"><span class=\"ez-toc-section\" id=\"A_Unified_Future\"><\/span><strong>A Unified Future?<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h3>\n\n\n\n<p>Ultimately, Scale Up and Scale Out represent a trade-off between performance and cost. As technology evolves, the boundary between the two is expected to blur. Emerging open Scale Up standards like ETH-X, based on Ethernet, offer promising performance metrics:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Switch chip capacity: Up to 51.2 Tbps<\/li>\n\n\n\n<li>SerDes speed: Up to 112 Gbps<\/li>\n\n\n\n<li>Latency: As low as 200 nanoseconds<\/li>\n<\/ul>\n\n\n\n<p>Since Scale Out also utilizes Ethernet, this convergence hints at a unified architecture, where a single standard may underpin both expansion models in future computing ecosystems.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"h-trends-in-ai-compute-cluster-development\"><span class=\"ez-toc-section\" id=\"Trends_in_AI_Compute_Cluster_Development\"><\/span><strong>Trends in AI Compute Cluster Development<\/strong><strong><\/strong><span class=\"ez-toc-section-end\"><\/span><\/h2>\n\n\n\n<p>As the field of artificial intelligence (AI) continues to expand, AI compute clusters are evolving along several key trajectories:<\/p>\n\n\n\n<p><strong>Geographical Distribution of Physical Infrastructure<\/strong><strong><\/strong><\/p>\n\n\n\n<p>AI clusters are scaling toward configurations containing tens or even hundreds of thousands of AI cards. For instance:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>NVIDIA\u2019s NVL72 rack integrates 72 chips.<\/li>\n\n\n\n<li>Huawei\u2019s CM384 deploys 384 chips across 16 racks.<\/li>\n<\/ul>\n\n\n\n<p>To build a 100,000-card cluster using Huawei&#8217;s CM384 architecture would require 432 CM384 units\u2014equaling 165,888 chips and 6,912 racks. Such a scale far exceeds the physical and electrical capacity of a single data center.<\/p>\n\n\n\n<p>As a result, the industry is actively exploring distributed data center deployments that can operate as a unified AI compute cluster. These architectures rely heavily on advanced data center interconnect (DCI) optical communication technologies, which must support long-distance, high-bandwidth, and low-latency transmission. Innovations such as hollow-core optical fiber are expected to accelerate in adoption.<\/p>\n\n\n\n<p><strong>Customization of Node Architecture<\/strong><strong><\/strong><\/p>\n\n\n\n<p>The traditional approach to building AI clusters often centered on maximizing the number of AI chips. However, there is a growing emphasis on deep architectural design, beyond sheer volume.<\/p>\n\n\n\n<p>Emerging trends include the pooling of computational resources\u2014such as GPUs, NPUs, CPUs, memory, and storage\u2014to create highly adaptable clusters tailored to the requirements of large-scale AI models, including architectures like Mixture of Experts (MoE).<\/p>\n\n\n\n<p>In short, delivering bare chips is no longer sufficient. Tailored, scenario-specific designs are increasingly necessary to ensure optimal performance and efficiency.<\/p>\n\n\n\n<p><strong>Intelligent Operations and Maintenance<\/strong><strong><\/strong><\/p>\n\n\n\n<p>Training large-scale AI models is notoriously error-prone, with failures potentially occurring within mere hours. Each failure necessitates retraining, prolonging development timelines and increasing operational costs.<\/p>\n\n\n\n<p>To mitigate these risks, organizations are prioritizing system reliability and stability by incorporating intelligent operation and maintenance tools. These systems can:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Predict potential faults<\/li>\n\n\n\n<li>Identify suboptimal or deteriorating hardware<\/li>\n\n\n\n<li>Enable proactive component replacement<\/li>\n<\/ul>\n\n\n\n<p>Such approaches significantly reduce failure rates and downtime, thereby bolstering cluster stability and effectively enhancing overall computational output.<\/p>\n\n\n\n<p><strong>Energy Efficiency and Sustainability<\/strong><strong><\/strong><\/p>\n\n\n\n<p>AI computation demands massive energy consumption, prompting leading vendors to explore strategies for reducing power usage and increasing reliance on renewable energy sources.<\/p>\n\n\n\n<p>This push for green compute clusters aligns with broader sustainability initiatives, such as China&#8217;s &#8220;East Data, West Compute&#8221; strategy, which aims to optimize energy allocation and promote long-term ecological development of AI infrastructure.<\/p>\n<style>\r\n\r\n        .lwrp.link-whisper-related-posts{\r\n            \r\n            margin-top: 40px;\nmargin-bottom: 30px;\r\n        }\r\n        .lwrp .lwrp-title{\r\n            \r\n            \r\n        }\r\n        .lwrp .lwrp-description{\r\n            \r\n            \r\n\r\n        }\r\n        .lwrp .lwrp-list-container{\r\n        }\r\n        .lwrp .lwrp-list-multi-container{\r\n            display: flex;\r\n        }\r\n        .lwrp .lwrp-list-double{\r\n            width: 48%;\r\n        }\r\n        .lwrp .lwrp-list-triple{\r\n            width: 32%;\r\n        }\r\n        .lwrp .lwrp-list-row-container{\r\n            display: flex;\r\n            justify-content: space-between;\r\n        }\r\n        .lwrp .lwrp-list-row-container .lwrp-list-item{\r\n            width: calc(100% - 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A common point of discussion surrounding AI is the concept of compute clusters\u2014one of the three foundational pillars of AI, alongside algorithms and data. These compute clusters serve as the primary source of computational power, akin to a massive [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":17795,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":"","_wpscppro_dont_share_socialmedia":false,"_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":"default","_instagram_share_type":"default","_medium_share_type":"","_threads_share_type":"","_google_business_share_type":"","_selected_social_profile":[{"id":"skM9ewvR8O","platform":"linkedin","platformKey":0,"name":"Jason Xue","type":"person","thumbnail_url":"https:\/\/media.licdn.com\/dms\/image\/C5603AQErPqKD0j6qBg\/profile-displayphoto-shrink_100_100\/0\/1599138392315?e=1723075200&v=beta&t=joEkh1OeKQ0F-QpAPv4xxQyBdGlHyccIQZauRSs6RvU","share_type":"default"}],"_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":[2,29],"tags":[],"class_list":["post-17695","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-networking"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v20.13 (Yoast SEO v25.8) - 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