Introduction

As organizations rapidly adopt cloud-native paradigms, managing infrastructure for scalable, secure, and high-availability AI/ML workloads is a new frontier. The convergence of IBM Fusion with Red Hat Validated Patterns provides a robust foundation for deploying advanced workloads such as Retrieval Augmented Generation (RAG) based large language models (LLMs)—even in environments where GPU resources are scarce or unavailable. In this article, I’ll guide you through deploying a RAG LLM demo pattern entirely on CPU infrastructure using IBM Fusion and Red Hat OpenShift.

The Challenge

Cloud-native applications and modern AI workloads typically demand sophisticated infrastructure handling, with stress on compute acceleration (GPUs), orchestration, and operational compliance. Not every enterprise environment provides ready access to GPUs—especially for proofs of concept, education, or edge/on-premises deployments. A solution that runs purely on CPU resources, yet enables advanced large language model capabilities, fills a critical gap.

Solution Overview: IBM Fusion and Red Hat Validated Patterns

IBM Fusion delivers a container-native hybrid cloud data platform, simplifying OpenShift deployments for stateful and stateless applications. Available both as software and as a hyper-converged appliance, Fusion unifies compute, storage, networking, and—optionally—GPU resources. A key benefit is enhanced data sovereignty, supporting privacy and compliance by ensuring sensitive data remains within jurisdictional boundaries.

Red Hat Validated Patterns provide automated blueprints—tested, reusable, and production-ready—for deploying complex solution stacks with best-practice configuration. Here, we focus on a validated RAG LLM pattern, explicitly designed for environments without GPU acceleration.

Architecture

The RAG LLM validated pattern leverages:         

• Vector Database: Stores content/document embeddings for efficient semantic search.
• Content Store: Supplies source documents, vectorized into the DB.
• LLM Inference Service: Uses `llama`-based models served over CPU via KServe, running natively on OpenShift.
• Demo Front End: Interactive UI for querying the chatbot and visualizing RAG processing.
• Hashi Corp Vault: Manages secrets (e.g., Hugging Face tokens, DB credentials) securely and automatically.
• Deployment Orchestration: Centralized via Argo CD and other Red Hat automation primitives.

The workflow ensures all data and operations remain within controlled, on-premises environments, with all automation handled seamlessly for efficiency and standardization.

RAG Process Recap

Retrieval Augmented Generation (RAG) combines two AI strengths

• Retrieval: Semantic extraction of relevant information from indexed document embeddings.
• Generation: Synthesis of the final response using an LLM, grounded in the retrieved content.

Step-by-Step Deployment Guide
Pre-requisites
   - An IBM Fusion environment with OpenShift cluster (admin privileges)
   - No GPU hardware required
   - [Optional] Familiarity with Argo CD, HashiCorp Vault

1. Fork and Clone the Validated Pattern Repository
# Fork the GitHub repository using the GitHub UI (https://github.com/ibm/rag-llm-cpu-pattern)

# Clone your fork to your working environment
git clone https://github.com/<your-org>/rag-llm-cpu-pattern.git
cd rag-llm-cpu-pattern

2. Generate and Configure Hugging Face Token
Obtain Token:
•	Go to https://huggingface.co
•	Profile > Access Tokens > New Token (read-only)

Integrate token into values-secret.yaml:
# values-secret.yaml

huggingface:
  token: 
  value: your_huggingface_token_here"

3. Install Pattern Using Provided Script
# ./pattern.sh make install
The script orchestrates deployment of all required components—vector DB, LLM inference, Vault, Argo CD, and the demo UI.
4. Validate Deployment
-	Access the OpenShift console and use the navigation menu to locate deployed components:
-	Argo CD (orchestration/dashboard)
-	RAG LLM Demo UI (end-user interaction)
-	HashiCorp Vault (secret store)
-	You should see the chat demo live and operational within your cluster, running CPU-only LLM inference.
5. Extending and Customizing
-	To broaden the chatbot’s context scope, update the documents housed in the vector DB:
-	Replace or augment default docs in your designated repo.
-	Re-run vectorization as needed.
-	Secrets (e.g., database credentials) are centrally managed by Vault—minimal manual intervention is needed.

Observations & Best Practices

• Compliance: The solution’s data locality and explicit infrastructure boundaries facilitate regulatory compliance—a necessity in many industries.
• Scalability and Resilience: IBM Fusion’s orchestration and automation empower IT teams to minimize operational overhead, allowing focus on AI innovation rather than “keeping the lights on.”

Recap
A sample demo set up as described above enables users to query the RAG chatbot directly via the UI. Note that by default, the pattern ships with a 
limited document set; for broader or production usage, expand the context corpus as required by your target use case.

Conclusion
Deploying a RAG LLM solution without GPUs is now practical and straightforward, thanks to IBM Fusion and Red Hat Validated Patterns. This pattern is 
ideal for pilots, educational roll-outs, or any environment where GPU resources are not presently available. As data, regulatory, and infrastructure 
demands evolve, solutions like these ensure your AI/ML innovations remain robust, secure, and scalable.

Deployment video