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.

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.