This lab is intended to give you an overview of HPE Private Cloud AI, HPE's turnkey solution for on-prem AI workloads. HPE has paired with NVIDIA's AI Enterprise software giving customers a scalable on-prem solution that can handle multiple different AI workloads from Inference, RAG, model fine-tuning, and model training.

Learn to deploy the Nous Research Hermes Agent, backed by NVIDIA Nemotron-3-Super-120B-A12B, as a persistent infrastructure co-pilot. This hands-on lab guides you through configuring persistent memory across multiple surfaces (the terminal, Mattermost, and a desktop app), enforcing custom engineering standards, and compiling repeatable infrastructure workflows into reusable skills.

Experience Cisco AI Defense in an interactive Capture the Flag (CTF), designed to showcase how Cisco is securing the future of GenAI.

Explore the hidden dangers of prompt injection in Large Language Models (LLMs). This lab reveals how attackers manipulate LLMs to disclose private information and behave in ways that they were not intended to. Discover the intricacies of direct and indirect prompt injection and learn to implement effective guardrails.

Watch a multi-agent AI system run network operations on a live Cisco leaf-spine fabric. It detects configuration drift against a golden baseline, diagnoses root cause from live telemetry, and proposes Ansible remediation — but executes nothing without your approval. Work five realistic incidents, from a fat-finger mistake to a deliberate attack, and judge where agentic automation belongs.

A hands-on lab where learners configure LiteLLM as a local reverse proxy that intelligently routes requests between multiple LLM backends — a general model for simple prompts, a coding model and a reasoning model for complex ones — instead of hard-coding a single model into an application.

Routing LLM Requests with NVIDIA Switchyard teaches config-driven LLM routing through a hands-on Docker Compose lab. Students deploy an OpenAI-compatible proxy plus dashboard, use passthrough routes, binary and category-based LLM classifiers, then measure real cost, latency, and quality tradeoffs between routed and frontier-baseline models across six progressive modules.

This lab will go into the basics of Retrieval Augmented Generation (RAG) through hands on access to a dedicated environment.

In this lab, customers will deploy the Intelligent Resource Optimizer on the HPE Private Cloud AI instance in the ATC. Built using the NVIDIA NeMo Agent Toolkit and powered by Open AI OSS NIMs, this solution moves beyond static analysis to proactive infrastructure management. By integrating a specialized Time Series model, the Intelligent Resource Optimizer analyzes historical system utilization to accurately forecast future resource demands. This allows IT Operations teams to receive data-driven recommendations for scaling and optimization, ensuring peak performance and cost-efficiency across the environment.

This lab will walk the lab user through the basics of Generative AI

In this lab, we'll be focusing on the programatic steps of Retrieval Augmented Generation (RAG). First, we'll discuss data chunking, how we break down our documents. Then, we'll explore how these chunks become embeddings, numerical representations. Finally, we'll see how a vector database helps us efficiently retrieve this information.

This lab presents a simplified two-switch high-availability design for a Cisco AI deployment, highlighting both North–South and East–West traffic flows. It uses NDFC to demonstrate operational visibility and telemetry monitoring. The provided NDFC account is read-only for demonstration purposes. The topology separates administrative connectivity and GPU communication, offering a focused view of core AI networking concepts.