A practical guide to InstructLab and RHEL AI

Learning path | 4 resources | 6 hrs and 30 mins | Published on July 22, 2025
Mriganka Paul

Transform your domain expertise into intelligent applications that deliver real business value with this step-by-step guide. Begin with InstructLab model customization and progress to enterprise-scale deployment on Red Hat's trusted AI platform.

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  1. Overview: A practical guide to InstructLab and RHEL AI
  2. Common development hurdles and Red Hat’s solution: An introduction Page | 3 hrs
  3. Model deployment and troubleshooting with RHEL AI Page | 2 hrs
  4. Scale with OpenShift AI and important considerations Page | 1 hr and 30 mins

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Recently, artificial intelligence has become a cornerstone of innovation for enterprises across industries. However, developing, deploying, and managing AI applications at scale presents significant challenges that many organizations struggle to overcome. Red Hat's comprehensive AI portfolio, including InstructLab, Red Hat Enterprise Linux (RHEL) AI, and Red Hat OpenShift AI, offers a powerful solution to these challenges, enabling a seamless developer experience while maintaining enterprise-grade reliability and security.

Prerequisites:

  • Technical requirements:
    • Development machine with Python 3.9+ and 16GB+ RAM.
    • GPU-enabled hardware (recommended for optimal model training performance).
    • Active Amazon Web Services (AWS) account with permissions to create EC2 instances, S3 buckets, and IAM roles.
    • Red Hat subscriptions for RHEL AI and OpenShift AI access.
    • OpenShift cluster (4.12+) with GPU nodes and sufficient storage.
  • Required knowledge:
    • Basic Linux command line experience.
    • Familiarity with Python and virtual environments.
    • Understanding of containerization concepts (Docker/Podman).
    • Basic cloud computing knowledge (AWS fundamentals).
    • YAML file editing and basic configuration management.
  • Tools and access:
    • AWS command-line interface (CLI) installed and configured.
    • OpenShift CLI (oc) and kubectl.
    • SSH key pairs for AWS instance access.
    • Container registry access (Quay.io or similar).
    • Git for taxonomy management.

In this learning path, you will:

  • Learn how Red Hat’s AI portfolio addresses challenges that organizations face in developing, deploying, and managing AI applications at scale.
  • Customize foundation models with domain-specific knowledge using InstructLab's intuitive taxonomy system.
  • Deploy and test models in a production-ready RHEL AI environment on AWS.
  • Serve models efficiently using the appropriate backend configuration for GGUF files.