Overview

The purpose of the AI Factory collection is to provide a step-by-step process for setting up a simple AI Factory system and getting it up and running quickly, including:

• Identify the minimum hardware and networking requirements for your AI Factory. These baseline specifications also serve as a reference for more advanced deployments. OpenNebula supports high-performance architectures such as InfiniBand, Spectrum-X, and NVLink, although these setups are not automated and require custom configuration.
• Follow the step-by-step deployment instructions using OneDeploy to build your AI Factory, with options for both on-premises installations and cloud-based deployments.
• Optionally validate your setup using the same methodology we apply during formal infrastructure acceptance. This validation focuses on using AI-ready Kubernetes with NVIDIA Dynamo® or NVIDIA KAI Scheduler®.

Basic Outline

Configuring, deploying and validating a high-performance AI infrastructure using OpenNebula involves these steps:

1. Familiarize with Architecture and Specifications.
2. Deploy and configure your AI Factory with one of these alternatives:
   • On-premises AI Factory Deployment: using OneDeploy for on-prem environments.
   • On-cloud AI Factory Deployment: using OneDeply on Scaleway for cloud environments.
3. Perform Validation: as a prerequisite, you must have an AI Factory ready to be validated. These are the options to validate your AI Factory:
   • Validation with LLM Inferencing : using vLLM with two different models and two model sizes, running across both H100 and L40S GPUs
   • Validation with AI-Ready Kubernetes: using H100 and L40S deployment to run Kubernetes. Once the AI-ready Kubernetes cluster is up, additional validation steps can be carried out, including::
     • Validation with NVIDIA Dynamo: integrating the GPU-powered Kubernetes cluster with the NVIDIA Dynamo Cloud Platform to provision and manage AI workloads through the Dynamo framework for your AI workloads on top of the NVIDIA Dynamo framework.
     • Validation with NVIDIA KAI Scheduler: using the NVIDIA KAI Scheduler to share GPU resources across different workloads within the AI-ready Kubernetes cluster.

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