Module 1: LLM Deployment Strategies

Introduction

This module covers deploying Red Hat Inference Server (vLLM) across three key enterprise platforms. Each deployment target serves different architectural needs and operational requirements.

Deployment Targets

RHEL (1.1): Bare metal and edge deployments requiring direct hardware control and minimal overhead. Ideal for single-node inference, air-gapped environments, or maximum performance scenarios.

OpenShift (1.2): Container orchestration for scalable, multi-node deployments. Best for cloud-native architectures, horizontal scaling, and DevOps integration workflows.

OpenShift AI (1.3): Managed AI platform providing MLOps capabilities, data science workflows, and enterprise governance. Optimal for integrated AI pipelines and managed model serving.

Learning Objectives

  • Deploy vLLM across three enterprise platforms

  • Understand platform-specific configuration requirements

  • Compare deployment architectures and operational trade-offs

  • Establish foundation for performance evaluation and optimization

Prerequisites

  • Access to target deployment environments

  • Basic familiarity with containers and Kubernetes concepts

  • Understanding of LLM serving requirements

Ready to deploy? Let’s start with platform-specific implementations.