RHEL AI combines open, more efficient models with accessible model alignment, extending the possibilities of AI innovation across the hybrid cloud
While gen AI’s promise is immense, the associated costs of procuring, training and fine-tuning LLMs can be astronomical, with some leading models costing nearly $200 million to train before launch. This does not include the cost of aligning for the specific requirements or data of a given organization, which typically requires data scientists or highly-specialized developers. No matter the model selected for a given application, alignment is still required to bring it in-line with company-specific data and processes, making efficiency and agility key for AI in actual production environments.
Red Hat believes that over the next decade, smaller, more efficient and built-to-purpose AI models will form a substantial mix of the enterprise IT stack, alongside cloud-native applications. But to achieve this, gen AI needs to be more accessible and available, from its costs to its contributors to where it can run across the hybrid cloud. For decades, open source communities have helped solve similar challenges for complex software problems through contributions from diverse groups of users; a similar approach can lower the barriers to effectively embracing gen AI.
An open source approach to gen AI
These are the challenges that RHEL AI intends to address – making gen AI more accessible, more efficient and more flexible to CIOs and enterprise IT organizations across the hybrid cloud. RHEL AI helps:
- Empower gen AI innovation with enterprise-grade, open source-licensed Granite models, and aligned with a wide variety of gen AI use cases.
- Streamline aligning gen AI models to business requirements with InstructLab tooling, making it possible for domain experts and developers within an organization to contribute unique skills and knowledge to their models even without extensive data science skills.
- Train and deploy gen AI anywhere across the hybrid cloud by providing all of the tools needed to tune and deploy models for production servers wherever associated data lives. RHEL AI also provides a ready on-ramp to Red Hat OpenShift AI for training, tuning and serving these models at scale while using the same tooling and concepts.
RHEL AI is also backed by the benefits of a Red Hat subscription, which includes trusted enterprise product distribution, 24×7 production support, extended model lifecycle support and Open Source Assurance legal protections.
RHEL AI extends across the hybrid cloud
Bringing a more consistent foundation model platform closer to where an organization’s data lives is crucial in supporting production AI strategies. As an extension of Red Hat’s hybrid cloud portfolio, RHEL AI will span nearly every conceivable enterprise environment, from on-premise datacenters to edge environments to the public cloud. This means that RHEL AI will be available directly from Red Hat, from Red Hat’s original equipment manufacturer (OEM) partners and to run on the world’s largest cloud providers, including Amazon Web Services (AWS), Google Cloud, IBM Cloud and Microsoft Azure. This enables developers and IT organizations to use the power of hyperscaler compute resources to build innovative AI concepts with RHEL AI.
