RHEL AI vs OpenShift AI vs IBM watsonx: Navigating the AI Ecosystem

Integrating AI into Enterprise Workflows made Simpler

6 min readJun 20, 2024

In today’s rapidly evolving digital landscape, generative AI is revolutionizing enterprises. It enables the creation of new content, enhances decision-making, and drives innovation across various industries. Integrating AI into business workflows not only boosts operational efficiency but also accelerates technical advancements. This article explores how Red Hat Enterprise Linux AI (RHEL AI), Red Hat OpenShift AI (RHOAI), and IBM watsonx can be leveraged together to maximize their potential.

Integrating RHEL AI, OpenShift AI, and IBM watsonx

To fully harness the power of generative AI, enterprises can integrate RHEL AI, OpenShift AI, and watsonx into their workflows. Here’s how these tools can be used together:

RHEL AI

Red Hat Enterprise Linux AI (RHEL AI) is a platform designed for developing, testing, and running open source generative AI models for enterprise applications. Built on the robust and stable Red Hat Enterprise Linux, it focuses on security, scalability, and ease of use. The platform supports fine-tuning AI models with domain-specific data, enabling businesses to tailor AI solutions to their unique needs. By leveraging an open source approach, RHEL AI lowers costs, removes barriers to testing and experimentation, and promotes innovation, making AI development more accessible and efficient for enterprises.

RHEL AI will be embedded inside of Red Hat OpenShift AI (RHOAI) and will provide a secure and optimized environment for AI development, with the necessary AI frameworks and libraries installed. It is designed to handle intensive computational tasks efficiently and is a part of IBM and Red Hat’s open AI strategy.

It incorporates the following advanced AI capabilities and features:

1. Open Granite Models:

Granite Models: Open-source, high-performance language and code models from IBM, Apache 2 licensed
Custom LLMs: Train available base models with custom data and choose to share or keep private

2. InstructLab Model Alignment:

InstructLab (iLAB) is an open-source AI project developed by IBM and Red Hat that enhances Large Language Models (LLMs) through community contributions. It is built upon the Large-scale Alignment for ChatBots (LAB) methodology, which is described in a 2024 research paper by members of the MIT-IBM Watson AI Lab and IBM Research. This approach fine-tunes large language models (LLMs) using a taxonomy-driven methodology and high-quality synthetic data generation. Here are the key features:

InstructLab (iLAB): Provides a command-line interface (CLI) that interacts with a local git repository of skills and knowledge.
Taxonomy-Driven Methodology: Organizes skills and knowledge systematically to enhance model customization as per specific business use-case.
Synthetic Data Generation: Generates synthetic data using a teacher model to enhance LLM training.
Validation Process: Validates synthetic data using a critic model to ensure accuracy and relevance.
Feedback Mechanism: Includes gates for human review and feedback, essential for refining and improving model performance.
PyTorch Integration: Utilizes PyTorch for optimized tensor computations on both GPUs and CPUs, enhancing model training efficiency.
DeepSpeed: Deep learning optimization suite for both training and inference, maximizing efficiency and scalability of AI workflows.
vLLM Inference Engine: High-throughput, memory-efficient inference and serving engine based on PyTorch, ensuring rapid deployment and operation of LLMs.
Community Collaboration: Open-source project with Apache 2.0 license, fostering contributions and innovation.

3. Optimized Infrastructure:

Distributed as a bootable container image (bootc), RHEL AI is optimized for deployment on bare metal or cloud instances as well as hardware accelerators (AMD, Intel, NVIDIA), ensuring flexibility and scalability.

4. Enterprise Support:

Provides enterprise-grade support, lifecycle management, and IP indemnification at general availability.

OpenShift AI

Red Hat OpenShift AI (RHOAI) enhances RHEL AI with Kubernetes-based containerized orchestration and scalability. It is a platform that empowers enterprises to develop and deploy AI-driven solutions efficiently across hybrid cloud environments, facilitating the creation and delivery of AI-enabled applications at scale with high reliability and security. Additionally, RHOAI supports model serving and hosting, making real-time AI inference and integration seamless. It has been embedded within IBM watsonx.ai.

Scalability:

Hybrid and Multi-Cloud: Scales AI workloads across hybrid and multi-cloud environments.
Containerization: Manages containerized AI workloads efficiently.

2. MLOps:

Lifecycle Management: Tools for AI/ML lifecycle from development to deployment and monitoring.
Automation: Facilitates the automation of training , deployment and inference pipelines for generative AI models.

3. Integration:

Cloud-Native Applications: Facilitates integration of AI models with cloud-native applications in production.
Distributed Training: Spreads training across multiple nodes for faster completion and higher throughput.

IBM watsonx

IBM watsonx is an AI and data platform that includes three core components and a set of AI assistants to support a complete enterprise AI/ML lifecycle.

It includes the IBM watsonx.ai component, which provides a enterprise-ready studio of integrated tools for working with generative AI capabilities and building machine learning models. It includes access to a variety of both open source and proprietary models, and other toolings as well for building end-to-end AI solutions.
The watsonx.governance component provides end-to-end monitoring for machine learning and generative AI models to accelerate responsible, transparent, and explainable AI workflows.
The watsonx.data enables users to scale AI and analytics with all their data, wherever it resides and without the need to migrate or recatalog. It also includes fit-for-purpose query engines to optimize data workloads, an integrated vector database to prepare data for retrieval augmented generation (RAG) and other AI use cases.

In Summary: While the goal of RHEL AI is to provide a cost-effective solution for augmenting large language models with additional skills and data through community collaboration, it also ensures portability across hybrid cloud environments. By leveraging Red Hat OpenShift AI, it enables the scaling of AI workflows, and with the integration of IBM watsonx, it offers enhanced capabilities for enterprise AI development, data management, and model governance.

Conclusion

IBM and Red Hat’s open AI strategy, including the use of open source Granite models and the InstructLab technique for fine-tuning LLMs, differentiates them in the market and drives awareness and relevancy for their AI offerings. This strategy will also benefit watsonx.ai clients, as it will embed OpenShift AI and RHEL AI while giving them access to a variety of open source and proprietary models, as well as enterprise-ready tooling and integration with other watsonx products. IBM and Red Hat are committed to open innovation and providing clients with the flexibility to deploy on hybrid cloud or on-premises environments.

By integrating RHEL AI, OpenShift AI, and watsonx.ai, enterprises can create a powerful AI ecosystem that leverages the strengths of each platform. This comprehensive approach not only maximizes the impact of AI on business processes but also ensures security, scalability, and compliance. Embracing these technologies will position businesses at the forefront of the AI revolution, ready to capitalize on the opportunities it presents.