AI296 – Develop, Test, and Run Granite Family LLMs with Red Hat Enterprise Linux AI — Lesson 1: RHEL AI Architecture, Granite Models, Runtime, Hardware, and Environment Setup

Learning Objectives

• Explain the architecture, purpose, dependencies, and expected behavior of RHEL AI architecture, Granite models, and runtime.
• Apply hardware resources and environment setup while preserving reviewable technical evidence.
• Recognize and correct the condition where an unstated prerequisite or ownership gap causes the initial design to be incomplete.

Detailed Topics

• RHEL AI architecture: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• Granite models: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• runtime: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• hardware resources: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.
• environment setup: apply the concept to the course scenario, identify dependencies and controls, and record objective evidence of the result.

Instructor Demonstration

The instructor demonstrates rhel ai architecture, granite models, runtime, hardware, and environment setup by tracing architecture, commands, configuration, system messages, and evidence. The walkthrough tests how an unstated prerequisite or ownership gap causes the initial design to be incomplete and explains how to isolate the cause without masking related issues.

Hands-On Exercise

Establish a controlled baseline for RHEL AI Architecture, Granite Models, Runtime, Hardware, and Environment Setup and add the verified result to the RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide.

1. Confirm scope, versions, access, dependencies, assumptions, and acceptance criteria.
2. Perform the workflow while recording commands, configuration, output, events, and decisions.
3. Test the normal path and one course-specific exception or boundary condition.
4. Retest the correction and document rollback, ownership, and follow-up actions.

Scenario Analysis

• Identify which dependency most strongly influences RHEL AI architecture in the stated scenario.
• Compare two approaches for hardware resources and explain the security, reliability, performance, and support trade-offs.
• Decide what evidence must be retained in the RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide before operational acceptance.

Troubleshooting Guidance

Begin with the first observable difference from the expected result. Check architecture, scope, identities, prerequisites, and starting conditions separately, then correlate logs, events, commands, metrics, and configuration history. Treat the assumption that an unstated prerequisite or ownership gap causes the initial design to be incomplete as a hypothesis until a reproducible retest confirms the cause.

Validation Checklist

• The required architecture, scope, identities, prerequisites, and starting conditions were checked and recorded.
• The normal path and a course-specific exception were tested.
• The RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide links conclusions to commands, configuration, logs, metrics, or other objective evidence.
• Corrective actions, rollback, ownership, and handover notes are complete.

Lesson Outcome

Learners can apply RHEL AI Architecture, Granite Models, Runtime, Hardware, and Environment Setup, explain the observed platform behavior, and support the result with a reviewable RHEL AI project, model-serving configuration, evaluation results, security checklist, and operations guide.