In this learning path we will take you through HPE Private Cloud AI or HPE PCAI. We will guide you through all the components that make up the solution such as HPE GreenLake, Private Cloud AI, HPE Morpheus VM Essentials, GreenLake for Files storage array, HPE Ezmeral Container Platfrom and Aruba/NVIDIA switches. We will also allow you to interact with some hands on labs that will take you into both of our physical HPE Private Cloud AI environments including a small and medium setup.

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.

NVIDIA AI Enterprise (NVAIE) offers a robust suite of AI tools for various applications, including reasoning, speech & translation, biomedical, content generation, and route planning. It features community, NVIDIA, and custom models. NVAIE provides essential microservices such as NIM and CUDA-X used for security advisory, enterprise support, cluster management, and infrastructure optimization. Designed for cloud, data centers, workstations, and edge environments, NVAIE ensures scalable, secure, and efficient AI deployment.

This Learning Series was created for NVIDIA DGX admins and operators to explore things you would use on Day 2 when administering your NVIDIA DGX SuperPOD and BasePOD environments with BCM (Base Command Manager). It will detail how to update firmware, patch systems, run jobs against the infrastructure, and integrate other parts into BCM (Switches, AD, Cloud, etc.).

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.

A server is a crucial piece of hardware in every organization's data center. Its primary role is to support the success of an organization's web-tier, mid-tier, database, and AI application stack from a networking, processing, and storage standpoint regardless if the application is deployed within a natively installed operating system, in a virtual machine, or as a cloud-native or edge-native application. The server components that make up this hardware system can vary depending on the specific needs and requirements of the organization. The main components commonly found in a server include CPU (central processing unit), Memory, Network Interface Cards, Storage Devices, and in some cases accelerators like DPUs (data processing unit) and GPUs (graphical processing unit). In this Learning Path engineers can learn about the latest solutions on the market today that they can integrate into their solutions to help drive their business forward most effectively and efficiently.

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.

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.