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.

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 DGX admins and operators to explore things you would use on Day 2 when administering your DGX Superpod and Base pod 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.).

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.