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AI Essentials
Welcome to the AI Essentials Learning Series. This series is designed to equip you with the knowledge and skills necessary to understand key aspects of Artificial Intelligence (AI). As AI continues to revolutionize industries and transform the way we live and work, it is crucial to understand the fundamental components that enable its remarkable performance. This series of learning paths will provide you with a comprehensive overview of AI, High Performance Storage, High Performance Networking, High Performance Compute, and Security, all tailored to the specific needs of AI applications.
Why Kubernetes is the Platform of Choice for Artificial Intelligence
This report explores Kubernetes' role in AI, analyzing its capabilities in managing AI workloads, its benefits over traditional infrastructure and its unique features that make it the platform of choice for AI.
Building for Success: A CTO's Guide to Generative AI
A strategic roadmap for Chief Technology Officers to align GenAI strategies with business goals, assess infrastructure needs, and identify the talent and skills needed to achieve sustainable GenAI transformation.
Facilities Infrastructure Priorities in the Age of AI
This report outlines the critical priorities for facilities and infrastructure leaders as they prepare for the next wave of AI-driven growth.
NVIDIA DGX BasePOD
In this learning path, we cover NVIDIA's DGX systems and BasePOD infrastructure, detailing the setup, licensing, and management of Base Command Manager and DGX OS for high-performance AI workloads. They explain hardware requirements, network configurations, and system provisioning, emphasizing efficient resource management, scalability, and optimized AI model training across NVIDIA's cutting-edge computing platforms.
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Facilities Infrastructure Priorities in the Age of AI
This report outlines the critical priorities for facilities and infrastructure leaders as they prepare for the next wave of AI-driven growth.
Introduction to NVIDIA NIM for LLM
This learning path introduces NVIDIA NIM for LLM microservices, covering its purpose, formats, and benefits. You'll explore deployment options via API Catalog, Docker, and Kubernetes, and complete hands-on labs for Docker and Kubernetes-based inference workflows—building skills to deploy, scale, and integrate GPU-optimized LLMs into enterprise applications.
AI Proving Ground: Cisco UCS C885A
Discover the latest addition to WWT's AI Proving Ground — the Cisco UCS C885A server. Technical Solutions Architect, Chris Braun, walks us through the unboxing of our newest hardware that is part of the Cisco Secure AI Factory with NVIDIA environment. Equipped with 8 NVIDIA H200 GPUs, this powerhouse is designed for LLM training, fine-tuning, inference, and Retrieval Augmented Generation (RAG).
Deploy NVIDIA NIM for LLM on Kubernetes
NVIDIA NIM revolutionizes AI deployment by encapsulating large language models in a scalable, containerized microservice. Seamlessly integrate with Kubernetes for optimized GPU performance, dynamic scaling, and robust CI/CD pipelines. Simplify complex model serving, focusing on innovation and intelligent feature development, ideal for enterprise-grade AI solutions.
Deploy NVIDIA NIM for LLM on Docker
This lab provides the learner with a hands-on, guided experience of deploying the NVIDIA NIM for LLM microservice in Docker.
NVIDIA Run:ai for Platform Engineers
Welcome to the NVIDIA Run:ai for Platform Engineers Learning Path! This learning path is designed to build both foundational knowledge and practical skills for platform engineers and administrators responsible for managing GPU resources at scale. It begins by introducing learners to the key components of the NVIDIA Run:ai platform, including its Control Plane and Cluster, and explains how NVIDIA Run:ai extends Kubernetes to orchestrate AI workloads efficiently. The learning path then covers essential topics such as authentication and role-based access, organizational management through projects and departments, and workload operations using assets, templates, and policies. Learners will also explore GPU fractioning to understand how NVIDIA Run:ai maximizes GPU utilization and ensures fair resource allocation across teams. All this builds toward a hands-on lab experience designed to reinforce your learning and give you practical experience working directly with NVIDIA Run:ai.
Cisco UCS and NVIDIA RTX PRO 6000 Server Edition: Powering the Next Wave of Enterprise AI
By combining NVIDIA RTX PRO™ 6000 Blackwell Server Edition with Cisco UCS servers, enterprises gain a powerful and scalable foundation for AI and visualization workloads.
Pure Storage GenAI Pod with NVIDIA
This environment provides a highly performant environment to test the deployment and tuning of different NVIDIA NIMs and Blueprints.
F5 BIG-IP Next for Kubernetes on NVIDIA BlueField-3 DPUs
This lab will guide and educate you on what the F5 BIG-IP Next for Kubernetes is, how it is deployed with NVIDIA DOCA, and how it will ultimately help show the gains in offloading traffic to DPUs instead of tying up resources for your applications on the CPU of the physical K8 host.
NVIDIA DGX SuperPOD and DGX BasePOD Day 3 Operations
This Learning Series was created for NVIDIA DGX admins and operators to explore things you would use on Day 3 when administering your NVIDIA DGX SuperPOD and BasePOD environments with BCM (Base Command Manager). It will go into advanced topics of cmshell, cloud bursting from BCM, HA for headnodes, IB setup and testing of worker nodes, active directory integrations, as well as advanced workload topics of deploying Kubernetes from Base Command Manager.
Why Kubernetes is the Platform of Choice for Artificial Intelligence
This report explores Kubernetes' role in AI, analyzing its capabilities in managing AI workloads, its benefits over traditional infrastructure and its unique features that make it the platform of choice for AI.
Pure Storage Enterprise AI in-a-Box with Intel Gaudi 3 and Iterate.ai
Iterate.ai's Generate platform pairs with Intel Xeon CPUs, Gaudi 3 accelerators, Pure Storage FlashBlade//S, and Milvus vector DB. Deployed via Kubernetes/Slurm, it scales quickly, needs minimal tuning, and runs Llama 3, Mistral, and Inflection to accelerate AI training, inference, and search for healthcare, life-science, and finance workloads.
Inside Cisco Live 2025: Go Beyond with Softchoice and World Wide Technology
Watch an exclusive webinar where we Go Beyond the headlines of Cisco Live 2025. We'll unpack the biggest announcements across Cisco's key focus areas —Security, Networking, and AI.
AI Proving Ground: Unboxing the NVIDIA DGX B200
Take a tour of the NVIDIA DGX B200. Technical Solutions Architect, Chris Braun, explains the new features of the NVIDIA Blackwell chipset. We will showcase how we are leveraging the NVIDIA DGX B200 to build learning paths, articles, proof of concepts, and discuss the use cases for educating our clients and internal staff.
AI's Invisible Bottleneck: Why AI Stalls at the Network, not the GPU
For many, AI success isn't limited by how many GPUs you can buy; it's limited by how fast those GPUs can talk to each other without tripping over the plumbing. In this episode of the AI Proving Ground Podcast, two of WWT's top networking minds —Justin van Schaik and Eric Fairfield — lay out the real choke points slowing AI projects to a crawl and how powerful, modernized network architectures are quietly rewriting the rulebook for scaling AI.
Person Tracking with Intel's AI Reference Kit
This lab focuses on implementing live person tracking using Intel's OpenVINO™, a toolkit for high-performance deep learning inference. The objective is to read frames from a video sequence, detect people within the frames, assign unique identifiers to each person, and track them as they move across frames. The tracking algorithm utilized here is Deep SORT (Simple Online and Realtime Tracking), an extension of SORT that incorporates appearance information along with motion for improved tracking accuracy.
Hidden Infrastructure Demands of Enterprise AI
As AI pushes the limits of traditional IT infrastructure, enterprises are racing to modernize their data centers. In this episode, Mike Parham and Bruce Gray walk us through the behind-the-scenes decisions that matter — from power and cooling challenges to GPU readiness and sustainability. Whether you're modernizing or starting from scratch, this conversation is your blueprint for AI-ready infrastructure.
Assessing AI Workloads: How to Choose the Right Environment for Enterprise AI
Where should your AI workloads run? It's one of the most overlooked questions in AI strategy. From surprising constraints around power, cooling and floor space, to the growing demand for GPU-as-a-Service models, this episode delivers a field-level view of the challenges enterprises face when moving from AI proof of concept to production. You'll hear why infrastructure readiness assessments are essential, how AI workloads differ from traditional IT, and what to consider before buying that next GPU cluster.
Hands-On Lab Workshop: Liqid Disaggregated Composable Infrastructure
Join us for the Virtual Lab Workshop! During this exclusive session, the host will introduce the Liqid Composable Disaggregated Infrastructure (CDI) Lab. This lab has been purpose-built to allow users to validate NVIDIA GPU and Intel Optane NVMe technology composition to Dell PowerEdge servers via software. This one-hour event is limited to 60 participants who will have the opportunity to engage in Q&A during the session. Attendees are encouraged to actively participate by launching the lab themselves alongside the host for a hands-on experience.
Liqid Upgrade from 3.4 to 3.5
In this video, we will guide you through upgrading your Liqid infrastructure from 3.4 to 3.5. The upgrade requires your Liqid stack to have some downtime, as the main portion updates your Liqid director server from Centos to Ubuntu. During this upgrade, all hosts and chassis will need to be powered off in order to complete the upgrade successfully.