https://research.ibm.com/labsA Purpose-Built AI Platform for Enterprises

Red Hat’s RHEL AI is more than just another AI release. This platform simplifies enterprise-wide adoption by delivering an optimized, bootable RHEL image tailored for hybrid cloud deployments. With seamless integration of Granite models and InstructLab tools, RHEL AI supports popular GPU accelerators from AMD, Intel, NVIDIA, and the NeMo frameworks. By offering optimized PyTorch runtimes, it ensures smooth, large-scale AI model deployment.

IBM’s LAB Methodology and Open-Source Integration

A key part of RHEL AI’s strength lies in its collaboration with IBM Research. The platform integrates IBM’s LAB methodology—a revolutionary approach using synthetic data and multiphase tuning to align AI/ML models. This eliminates the need for costly manual adjustments, allowing developers to easily contribute to and refine large language models (LLMs) via the InstructLab project.

Furthermore, IBM Research has released select Granite English language and code models under an open-source Apache license. Now, developers can work on the Granite 7B English language model collaboratively, improving its capabilities for widespread enterprise use.

Scaling AI with OpenShift and Retrieval-Augmented Generation (RAG)

It integrates tightly with OpenShift AI, Red Hat’s platform for machine learning operations (MLOps). This allows large-scale AI model deployment within distributed Kubernetes clusters. RHEL AI also uses Retrieval-Augmented Generation (RAG) to enhance LLM performance, enabling models to access external knowledge bases for more accurate results.

Cost-Effective AI for Enterprises

Training large language models (LLMs) can cost millions, but  it helps bring these costs down through efficient use of resources. With RAG, users can train this AI using in-house subject matter experts, eliminating the need for extensive machine learning expertise. This democratizes the process, allowing businesses to benefit from AI without massive upfront costs.

Flexible Deployment and Cloud Integration

RHEL AI isn’t tied to a single environment. Whether you deploy it on-premise, at the edge, or in the public cloud, RHEL AI adapts to your data infrastructure. This flexibility ensures that businesses can implement AI strategies without overhauling existing systems.

The platform is already available on Amazon Web Services (AWS) and IBM Cloud as a “bring your own” (BYO) subscription offering. In the coming months, RHEL AI will also be available as a service on AWS, Google Cloud Platform (GCP), IBM Cloud, and Microsoft Azure.

Dell Collaboration and Future Prospects

Dell Technologies has partnered with Red Hat to bring RHEL AI to Dell PowerEdge servers. This collaboration aims to simplify AI deployments by offering validated hardware solutions, including NVIDIA accelerated computing.

According to Joe Fernandes, Vice President of Red Hat’s Foundation Model Platform, “RHEL AI provides the ability for domain experts, not just data scientists, to contribute to a built-for-purpose gen AI model across the hybrid cloud while enabling IT organizations to scale these models for production through Red Hat OpenShift AI.”

Conclusion: RHEL AI’s Impact on the Future of AI

As enterprises continue to expand their AI deployment efforts, RHEL AI stands out as a powerful, flexible, and cost-effective platform. By combining open-source innovation with enterprise-grade features, Red Hat is positioning itself as a leader in the rapidly evolving AI landscape. For businesses looking to harness the power of AI, this AI could very well be the solution that bridges the gap between expensive AI giants and accessible, practical AI applications.