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  <channel>
    <title>Whitepapers</title>
    <link>https://www.wiwynn.com/whitepapers</link>
    <description>Access comprehensive whitepapers on advanced data center solutions, cooling technologies, and AI acceleration to enhance efficiency and performance. Explore innovative strategies and best practices.</description>
    <language>en</language>
    <pubDate>Tue, 17 Mar 2026 08:00:00 GMT</pubDate>
    <dc:date>2026-03-17T08:00:00Z</dc:date>
    <dc:language>en</dc:language>
    <item>
      <title>White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI</title>
      <link>https://www.wiwynn.com/whitepapers/gpu-initiated-liquid-cooled-ultra-high-density-storage-for-next-gen-ai</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/gpu-initiated-liquid-cooled-ultra-high-density-storage-for-next-gen-ai" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/GPU-Initiated_TM_updated.jpg" alt="White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="color: #1f1f1f; background-color: #ffffff;"&gt;&lt;span&gt;This paper introduces a paradigm shift in storage architecture designed to overcome the CPU-centric data path bottlenecks in modern AI workloads&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;By combining NVIDIA SCADA (Scaled Accelerated Data Access) with a 100% liquid-cooled, fanless chassis housing 96 E3.S NVMe drives, we have created an ultra-high-density storage solution&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&amp;nbsp;&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/gpu-initiated-liquid-cooled-ultra-high-density-storage-for-next-gen-ai" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/GPU-Initiated_TM_updated.jpg" alt="White Paper: GPU-Initiated, Liquid-Cooled, Ultra-High-Density Storage for Next-Gen AI" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="color: #1f1f1f; background-color: #ffffff;"&gt;&lt;span&gt;This paper introduces a paradigm shift in storage architecture designed to overcome the CPU-centric data path bottlenecks in modern AI workloads&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;By combining NVIDIA SCADA (Scaled Accelerated Data Access) with a 100% liquid-cooled, fanless chassis housing 96 E3.S NVMe drives, we have created an ultra-high-density storage solution&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&amp;nbsp;&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fgpu-initiated-liquid-cooled-ultra-high-density-storage-for-next-gen-ai&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Tue, 17 Mar 2026 08:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/gpu-initiated-liquid-cooled-ultra-high-density-storage-for-next-gen-ai</guid>
      <dc:date>2026-03-17T08:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance</title>
      <link>https://www.wiwynn.com/whitepapers/kv-cache-offload-to-improve-ai-inferencing-cost-and-performance</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/kv-cache-offload-to-improve-ai-inferencing-cost-and-performance" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/KV%20Cache.jpg" alt="White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="color: #1f1f1f; background-color: #ffffff;"&gt;This paper explores &lt;span&gt;a disaggregated key-value (KV) storage architecture designed to efficiently offload KV cache tensors for generative AI workloads&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;By synergizing Wiwynn's OCP ORv3-compliant servers with Pliops' hardware-accelerated data path, this framework delivers a highly scalable and cost-effective solution for AI inferencing&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&amp;nbsp;&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/kv-cache-offload-to-improve-ai-inferencing-cost-and-performance" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/KV%20Cache.jpg" alt="White Paper: KV Cache Offload to Improve AI Inferencing Cost and Performance" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="color: #1f1f1f; background-color: #ffffff;"&gt;This paper explores &lt;span&gt;a disaggregated key-value (KV) storage architecture designed to efficiently offload KV cache tensors for generative AI workloads&lt;/span&gt;&lt;span&gt;. &lt;/span&gt;&lt;span&gt;By synergizing Wiwynn's OCP ORv3-compliant servers with Pliops' hardware-accelerated data path, this framework delivers a highly scalable and cost-effective solution for AI inferencing&lt;/span&gt;&lt;span&gt;.&lt;/span&gt;&amp;nbsp;&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fkv-cache-offload-to-improve-ai-inferencing-cost-and-performance&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Mon, 16 Mar 2026 08:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/kv-cache-offload-to-improve-ai-inferencing-cost-and-performance</guid>
      <dc:date>2026-03-16T08:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>Autonomous AI Agent for End-to-End Component Data Extraction</title>
      <link>https://www.wiwynn.com/whitepapers/autonomous-ai-agent-for-end-to-end-component-data-extraction</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/autonomous-ai-agent-for-end-to-end-component-data-extraction" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Autonomous_AI_Agent_for_End-to-End_Component_Data_Extraction.jpg" alt="Autonomous AI Agent&amp;nbsp;for End-to-End Component Data Extraction" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="color: #1f1f1f; background-color: #ffffff;"&gt;This paper explores an advanced framework designed to automate the extraction of important attributes from unstructured part datasheets. By synergizing expert data science preprocessing with the Retrieval-Augmented Generation (RAG) architecture, we have achieved industry-leading recognition accuracy.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/autonomous-ai-agent-for-end-to-end-component-data-extraction" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Autonomous_AI_Agent_for_End-to-End_Component_Data_Extraction.jpg" alt="Autonomous AI Agent&amp;nbsp;for End-to-End Component Data Extraction" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p style="color: #1f1f1f; background-color: #ffffff;"&gt;This paper explores an advanced framework designed to automate the extraction of important attributes from unstructured part datasheets. By synergizing expert data science preprocessing with the Retrieval-Augmented Generation (RAG) architecture, we have achieved industry-leading recognition accuracy.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fautonomous-ai-agent-for-end-to-end-component-data-extraction&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Fri, 13 Mar 2026 07:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/autonomous-ai-agent-for-end-to-end-component-data-extraction</guid>
      <dc:date>2026-03-13T07:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: From Design to Live Operation: Wiwynn’s L12 AI Cluster Deployment with MLPerf Validation</title>
      <link>https://www.wiwynn.com/whitepapers/from-design-to-live-operation-wiwynn-l12-ai-cluster-deployment-with-mlperf-validation</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/from-design-to-live-operation-wiwynn-l12-ai-cluster-deployment-with-mlperf-validation" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/From_Design_to_Live_Operation_Wiwynn_L12_AI_Cluster_Deployment_with_MLPerf_Validation.jpg" alt="White Paper: From Design to Live Operation: Wiwynn’s L12 AI Cluster Deployment with MLPerf Validation" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;Deploying large-scale AI clusters introduces engineering challenges that extend well beyond the individual server rack. From liquid cooling integration to high-voltage power distribution and network topology design, the "Last Mile" of AI infrastructure presents multifaceted integration complexities. &lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/from-design-to-live-operation-wiwynn-l12-ai-cluster-deployment-with-mlperf-validation" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/From_Design_to_Live_Operation_Wiwynn_L12_AI_Cluster_Deployment_with_MLPerf_Validation.jpg" alt="White Paper: From Design to Live Operation: Wiwynn’s L12 AI Cluster Deployment with MLPerf Validation" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;Deploying large-scale AI clusters introduces engineering challenges that extend well beyond the individual server rack. From liquid cooling integration to high-voltage power distribution and network topology design, the "Last Mile" of AI infrastructure presents multifaceted integration complexities. &lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Ffrom-design-to-live-operation-wiwynn-l12-ai-cluster-deployment-with-mlperf-validation&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Wed, 31 Dec 2025 06:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/from-design-to-live-operation-wiwynn-l12-ai-cluster-deployment-with-mlperf-validation</guid>
      <dc:date>2025-12-31T06:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: AI Rack Management with Wiwynn UMS</title>
      <link>https://www.wiwynn.com/whitepapers/ai-rack-management-with-wiwynn-ums</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/ai-rack-management-with-wiwynn-ums" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/AI_Rack_Management_with_Wiwynn_UMS_1.jpg" alt="White Paper: AI Rack Management with Wiwynn UMS" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;This paper discusses the rapid expansion of AI workloads and the resulting transformation in data center infrastructure requirements. Traditional air-cooling systems are becoming less effective due to the increased power density of server racks. To address this challenge, the paper presents Wiwynn's Universal Management System (UMS) as a solution for managing AI server racks and Direct Liquid Cooling (DLC) infrastructure.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/ai-rack-management-with-wiwynn-ums" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/AI_Rack_Management_with_Wiwynn_UMS_1.jpg" alt="White Paper: AI Rack Management with Wiwynn UMS" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;This paper discusses the rapid expansion of AI workloads and the resulting transformation in data center infrastructure requirements. Traditional air-cooling systems are becoming less effective due to the increased power density of server racks. To address this challenge, the paper presents Wiwynn's Universal Management System (UMS) as a solution for managing AI server racks and Direct Liquid Cooling (DLC) infrastructure.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fai-rack-management-with-wiwynn-ums&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Mon, 20 Oct 2025 07:25:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/ai-rack-management-with-wiwynn-ums</guid>
      <dc:date>2025-10-20T07:25:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: Introduction of a new Firmware Update Workflow for PLDM &amp; Redfish</title>
      <link>https://www.wiwynn.com/whitepapers/introduction-of-a-new-firmware-update-workflow-for-pldm-and-redfish</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/introduction-of-a-new-firmware-update-workflow-for-pldm-and-redfish" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Introduction_to_a_New_Workflow_of_Firmware_Update_for_PLDM_%26_Redfish_1.jpg" alt="White Paper: Introduction of a new Firmware Update Workflow for PLDM &amp;amp; Redfish" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;Firmware updates are essential for the BMC system. Each device requires a unique update flow and utilizes different transport protocols, such as I2C or JTAG. In previous projects, various update utilities had to be developed to accommodate these requirements. However, without proper software design, issues such as overly customized update tools and duplicated code—especially for hardware protocol read/write functions—have emerged. Therefore, a well-designed architecture for firmware updates is necessary to resolve these problems.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/introduction-of-a-new-firmware-update-workflow-for-pldm-and-redfish" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Introduction_to_a_New_Workflow_of_Firmware_Update_for_PLDM_%26_Redfish_1.jpg" alt="White Paper: Introduction of a new Firmware Update Workflow for PLDM &amp;amp; Redfish" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;Firmware updates are essential for the BMC system. Each device requires a unique update flow and utilizes different transport protocols, such as I2C or JTAG. In previous projects, various update utilities had to be developed to accommodate these requirements. However, without proper software design, issues such as overly customized update tools and duplicated code—especially for hardware protocol read/write functions—have emerged. Therefore, a well-designed architecture for firmware updates is necessary to resolve these problems.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fintroduction-of-a-new-firmware-update-workflow-for-pldm-and-redfish&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Fri, 17 Oct 2025 05:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/introduction-of-a-new-firmware-update-workflow-for-pldm-and-redfish</guid>
      <dc:date>2025-10-17T05:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: Beyond the Rack - The Elastic Management Framework for AI Data Centers</title>
      <link>https://www.wiwynn.com/whitepapers/beyond-the-rack-the-elastic-management-framework-for-ai-data-centers</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/beyond-the-rack-the-elastic-management-framework-for-ai-data-centers" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Beyond_the_Rack_The_Elastic_Management_Framework_for_AI_Data_Centers_.jpg" alt="White Paper: Beyond the Rack - The Elastic Management Framework for AI Data Centers" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;AI clusters using next-generation accelerators (e.g., NVIDIA GB200) push rack power density beyond 130 kW, making air cooling insufficient and driving adoption of direct liquid cooling (DLC). This whitepaper introduces Wiwynn’s Elastic Management Framework, a modular, scalable, interoperable architecture that unifies rack- and cluster-level monitoring and control across IT and facilities. The Wiwynn Universal Management System (UMS) enables autonomous rack-level protection and in-row CDU coordination, integrates heterogeneous sensors and controls via Redfish/Modbus/SNMP/analog/GPIO, and exports normalized telemetry to a Prometheus/Thanos platform with Grafana visualization and AlertManager response.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/beyond-the-rack-the-elastic-management-framework-for-ai-data-centers" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Beyond_the_Rack_The_Elastic_Management_Framework_for_AI_Data_Centers_.jpg" alt="White Paper: Beyond the Rack - The Elastic Management Framework for AI Data Centers" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;AI clusters using next-generation accelerators (e.g., NVIDIA GB200) push rack power density beyond 130 kW, making air cooling insufficient and driving adoption of direct liquid cooling (DLC). This whitepaper introduces Wiwynn’s Elastic Management Framework, a modular, scalable, interoperable architecture that unifies rack- and cluster-level monitoring and control across IT and facilities. The Wiwynn Universal Management System (UMS) enables autonomous rack-level protection and in-row CDU coordination, integrates heterogeneous sensors and controls via Redfish/Modbus/SNMP/analog/GPIO, and exports normalized telemetry to a Prometheus/Thanos platform with Grafana visualization and AlertManager response.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fbeyond-the-rack-the-elastic-management-framework-for-ai-data-centers&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Wed, 15 Oct 2025 01:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/beyond-the-rack-the-elastic-management-framework-for-ai-data-centers</guid>
      <dc:date>2025-10-15T01:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: Power Efficiency Optimization in AI Systems</title>
      <link>https://www.wiwynn.com/whitepapers/power-efficiency-optimization-in-ai-systems</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/power-efficiency-optimization-in-ai-systems" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Power_Efficiency_Optimization_in_AI_Systems.jpg" alt="White Paper: Power Efficiency Optimization in AI Systems" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;This whitepaper examines the growing importance of power efficiency in AI systems, where increasing computational demand translates into significant energy consumption and operating costs. We begin by introducing the overall architecture of AI applications and their critical power components, including power shelves and bricks, and the key part of switching converter efficiency analysis in detail.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/power-efficiency-optimization-in-ai-systems" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Power_Efficiency_Optimization_in_AI_Systems.jpg" alt="White Paper: Power Efficiency Optimization in AI Systems" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;This whitepaper examines the growing importance of power efficiency in AI systems, where increasing computational demand translates into significant energy consumption and operating costs. We begin by introducing the overall architecture of AI applications and their critical power components, including power shelves and bricks, and the key part of switching converter efficiency analysis in detail.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fpower-efficiency-optimization-in-ai-systems&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Tue, 14 Oct 2025 01:30:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/power-efficiency-optimization-in-ai-systems</guid>
      <dc:date>2025-10-14T01:30:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: General Guidance for Transitioning from Single-Phase to Two-Phase Liquid Cooling Solutions</title>
      <link>https://www.wiwynn.com/whitepapers/general-guidance-for-transitioning-from-single-phase-to-two-phase-liquid-cooling-solutions</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/general-guidance-for-transitioning-from-single-phase-to-two-phase-liquid-cooling-solutions" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/General_Guidance_for_Transitioning_from_Single-Phase_to_Two-Phase_Liquid_Cooling_Solutions.jpg" alt="White Paper: General Guidance for Transitioning from Single-Phase to Two-Phase Liquid Cooling Solutions" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;The rapid expansion of artificial intelligence, high-performance computing, and cloud services is driving unprecedented levels of heat generation in modern data centers. Traditional air cooling and single-phase liquid cooling are increasingly unable to meet the demands of high-density server racks and powerful processors. Two-phase liquid cooling, which transfers heat through the phase change of the coolant, offers significantly greater thermal efficiency and the potential for substantial energy savings.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/general-guidance-for-transitioning-from-single-phase-to-two-phase-liquid-cooling-solutions" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/General_Guidance_for_Transitioning_from_Single-Phase_to_Two-Phase_Liquid_Cooling_Solutions.jpg" alt="White Paper: General Guidance for Transitioning from Single-Phase to Two-Phase Liquid Cooling Solutions" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;The rapid expansion of artificial intelligence, high-performance computing, and cloud services is driving unprecedented levels of heat generation in modern data centers. Traditional air cooling and single-phase liquid cooling are increasingly unable to meet the demands of high-density server racks and powerful processors. Two-phase liquid cooling, which transfers heat through the phase change of the coolant, offers significantly greater thermal efficiency and the potential for substantial energy savings.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Fgeneral-guidance-for-transitioning-from-single-phase-to-two-phase-liquid-cooling-solutions&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Mon, 13 Oct 2025 07:00:00 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/general-guidance-for-transitioning-from-single-phase-to-two-phase-liquid-cooling-solutions</guid>
      <dc:date>2025-10-13T07:00:00Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
    <item>
      <title>White Paper: Testing Apparatus to Facilitate Validation in Immersion Cooling Environment</title>
      <link>https://www.wiwynn.com/whitepapers/testing-apparatus-to-facilitate-validation-in-immersion-cooling-environment</link>
      <description>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/testing-apparatus-to-facilitate-validation-in-immersion-cooling-environment" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Testing_Apparatus_to_Facilitate_Validation_in_Immersion_Cooling_Environment.jpg" alt="White Paper: Testing Apparatus to Facilitate Validation in Immersion Cooling Environment" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;As the chip industry moves beyond an era of rapid development, immersion cooling has emerged as a critical technology thanks to its superior cooling efficiency. Yet, validation across different environments often produces inconsistent results. In traditional immersion cooling setups, servers are typically installed vertically, which makes electrical and signal integrity (SI) validation difficult due to limited tank space. To address this, servers usually need to be removed and tested horizontally outside the tank.&lt;/p&gt;</description>
      <content:encoded>&lt;div class="hs-featured-image-wrapper"&gt; 
 &lt;a href="https://www.wiwynn.com/whitepapers/testing-apparatus-to-facilitate-validation-in-immersion-cooling-environment" title="" class="hs-featured-image-link"&gt; &lt;img src="https://www.wiwynn.com/hubfs/Whitepapers/Testing_Apparatus_to_Facilitate_Validation_in_Immersion_Cooling_Environment.jpg" alt="White Paper: Testing Apparatus to Facilitate Validation in Immersion Cooling Environment" class="hs-featured-image" style="width:auto !important; max-width:50%; float:left; margin:0 15px 15px 0;"&gt; &lt;/a&gt; 
&lt;/div&gt; 
&lt;p&gt;As the chip industry moves beyond an era of rapid development, immersion cooling has emerged as a critical technology thanks to its superior cooling efficiency. Yet, validation across different environments often produces inconsistent results. In traditional immersion cooling setups, servers are typically installed vertically, which makes electrical and signal integrity (SI) validation difficult due to limited tank space. To address this, servers usually need to be removed and tested horizontally outside the tank.&lt;/p&gt;  
&lt;img src="https://track-na2.hubspot.com/__ptq.gif?a=22200375&amp;amp;k=14&amp;amp;r=https%3A%2F%2Fwww.wiwynn.com%2Fwhitepapers%2Ftesting-apparatus-to-facilitate-validation-in-immersion-cooling-environment&amp;amp;bu=https%253A%252F%252Fwww.wiwynn.com%252Fwhitepapers&amp;amp;bvt=rss" alt="" width="1" height="1" style="min-height:1px!important;width:1px!important;border-width:0!important;margin-top:0!important;margin-bottom:0!important;margin-right:0!important;margin-left:0!important;padding-top:0!important;padding-bottom:0!important;padding-right:0!important;padding-left:0!important; "&gt;</content:encoded>
      <category>Whitepapers</category>
      <pubDate>Mon, 04 Aug 2025 02:36:13 GMT</pubDate>
      <guid>https://www.wiwynn.com/whitepapers/testing-apparatus-to-facilitate-validation-in-immersion-cooling-environment</guid>
      <dc:date>2025-08-04T02:36:13Z</dc:date>
      <dc:creator>Press</dc:creator>
    </item>
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