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    <title>Deployment on </title>
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    <lastBuildDate>Tue, 21 Oct 2025 00:00:00 +0000</lastBuildDate>
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      <title>On-premises AI Factory Deployment</title>
      <link>http://51.159.138.137:8082/devel/solutions/ai_factory_blueprints/deployment/cd_on-premises/</link>
      <pubDate>Tue, 21 Oct 2025 00:00:00 +0000</pubDate>
      <guid>http://51.159.138.137:8082/devel/solutions/ai_factory_blueprints/deployment/cd_on-premises/</guid>
      <description>&lt;p&gt;&lt;a id=&#34;cd_on-premises&#34;&gt;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;Machine Learning (ML) training and inference are resource-intensive tasks that often require the full power of a dedicated GPU. PCI passthrough allows a Virtual Machine (VM) to have exclusive access to a physical GPU, delivering bare-metal performance for the most demanding AI workloads.&lt;/p&gt;&#xA;&lt;p&gt;In this guide you will find the details to deploy and configure an AI-ready OpenNebula cloud using the &lt;a href=&#34;https://github.com/OpenNebula/one-deploy&#34;&gt;OneDeploy&lt;/a&gt; tool. It covers the general process of preparing an environment for demanding AI workloads by leveraging PCI passthrough for GPUs such as the NVIDIA H100 and L40S.&lt;/p&gt;</description>
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      <title>On-cloud AI Factory Deployment with Scaleway</title>
      <link>http://51.159.138.137:8082/devel/solutions/ai_factory_blueprints/deployment/cd_cloud/</link>
      <pubDate>Tue, 21 Oct 2025 00:00:00 +0000</pubDate>
      <guid>http://51.159.138.137:8082/devel/solutions/ai_factory_blueprints/deployment/cd_cloud/</guid>
      <description>&lt;p&gt;&lt;a id=&#34;cd_cloud&#34;&gt;&lt;/a&gt;&lt;/p&gt;&#xA;&lt;p&gt;This document describes the procedure to deploy an AI-ready OpenNebula cloud using OneDeploy on a single &lt;a href=&#34;https://www.scaleway.com/en/elastic-metal/&#34;&gt;Scaleway Elastic Metal&lt;/a&gt; bare-metal server equipped with GPUs.&lt;/p&gt;&#xA;&lt;p&gt;The architecture is a converged OpenNebula installation, where the frontend services and KVM hypervisor run on the same physical host. This approach is ideal for demonstrations, proofs-of-concept (PoCs), or for quickly trying out the solution without the need for a complex physical infrastructure.&lt;/p&gt;&#xA;&lt;p&gt;The outlined procedure is based on an instance with NVIDIA L40S GPUs as an example.&lt;/p&gt;</description>
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