Red Hat, Inc. announced the launch of Red Hat Enterprise Linux AI (RHEL AI), a foundation model platform that enables users to more seamlessly develop, test and deploy generative AI (GenAI) models. RHEL AI brings together the open source-licensed Granite large language model (LLM) family from IBM Research, InstructLab model alignment tools based on the LAB (Large-scale Alignment for chatBots) methodology and a community-driven approach to model development through the InstructLab project. The entire solution is packaged as an optimised, bootable RHEL image for individual server deployments across the hybrid cloud and is also included as part of OpenShift AI, Red Hat’s hybrid machine learning operations (MLOps) platform, for running models and InstructLab at scale across distributed cluster environments.
The launch of ChatGPT generated tremendous interest in GenAI, with the pace of innovation only accelerating since then. Enterprises have begun moving from early evaluations of GenAI services to building out AI-enabled applications. A rapidly growing ecosystem of open model options has spurred further AI innovation and illustrated that there won’t be “one model to rule them all.” Customers will benefit from an array of choices to address specific requirements, all of which stand to be further accelerated by an open approach to innovation.
Implementing an AI strategy requires more than simply selecting a model; technology organisations need the expertise to tune a given model for their specific use case, as well as deal with the significant costs of AI implementation. The scarcity of data science skills are compounded by substantial financial requirements including:
- Procuring AI infrastructure or consuming AI services
- The complex process of tuning AI models for specific business needs
- Integrating AI into enterprise applications
- Managing both the application and model lifecycle.
To truly lower the entry barriers for AI innovation, enterprises need to be able to expand the roster of who can work on AI initiatives while simultaneously getting these costs under control. With InstructLab alignment tools, Granite models and RHEL AI, Red Hat aims to apply the benefits of true open source projects – freely accessible and reusable, transparent and open to contributions – to GenAI in an effort to remove these obstacles.
Building AI in the open with InstructLab
IBM Research created the Large-scale Alignment for chatBots (LAB) technique, an approach for model alignment that uses taxonomy-guided synthetic data generation and a novel multi-phase tuning framework. This approach makes AI model development more open and accessible to all users by reducing reliance on expensive human annotations and proprietary models. Using the LAB method, models can be improved by specifying skills and knowledge attached to a taxonomy, generating synthetic data from that information at scale to influence the model and using the generated data for model training.
After seeing that the LAB method could help significantly improve model performance, IBM and Red Hat decided to launch InstructLab, an open source community built around the LAB method and the open source Granite models from IBM. The InstructLab project aims to put LLM development into the hands of developers by making, building and contributing to an LLM as simple as contributing to any other open source project.
As part of the InstructLab launch, IBM has also released a family of select Granite English language and code models in the open. These models are released under an Apache licence with transparency on the datasets used to train these models. The Granite 7B English language model has been integrated into the InstructLab community, where end users can contribute the skills and knowledge to collectively enhance this model, just as they would when contributing to any other open source project. Similar support for Granite code models within InstructLab will be available soon.
Open source AI innovation on a trusted Linux backbone
RHEL AI builds on this open approach to AI innovation, incorporating an enterprise-ready version of the InstructLab project and the Granite language and code models along with the world’s leading enterprise Linux platform to simplify deployment across a hybrid infrastructure environment. This creates a foundation model platform for bringing open source-licensed GenAI models into the enterprise. RHEL AI includes:
- Open source-licensed Granite language and code models that are supported and indemnified by Red Hat.
- A supported, lifecycled distribution of InstructLab that provides a scalable, cost-effective solution for enhancing LLM capabilities and making knowledge and skills contributions accessible to a much wider range of users.
- Optimised bootable model runtime instances with Granite models and InstructLab tooling packages as bootable RHEL images via RHEL image mode, including optimised Pytorch runtime libraries and accelerators for AMD Instinct™ MI300X, Intel and NVIDIA GPUs and NeMo frameworks.
- Red Hat’s complete enterprise support and lifecycle promise that starts with a trusted enterprise product distribution, 24×7 production support and extended lifecycle support.
As organisations experiment and tune new AI models on RHEL AI, they have a ready on-ramp for scaling these workflows with Red Hat OpenShift AI, which will include RHEL AI, and where they can leverage OpenShift’s Kubernetes engine to train and serve AI models at scale and OpenShift AI’s integrated MLOps capabilities to manage the model lifecycle. IBM’s watsonx.ai enterprise studio, which is built on Red Hat OpenShift AI today, will benefit from the inclusion of RHEL AI in OpenShift AI upon availability, bringing additional capabilities for enterprise AI development, data management, model governance and improved price performance.
The cloud is hybrid. So is AI.
For more than 30 years, open source technologies have paired rapid innovation with greatly reduced IT costs and lowered barriers to innovation. Red Hat has been leading this charge for nearly as long, from delivering open enterprise Linux platforms with RHEL in the early 2000s to driving containers and Kubernetes as the foundation for open hybrid cloud and cloud-native computing with Red Hat OpenShift.
This drive continues with Red Hat powering AI/ML strategies across the open hybrid cloud, enabling AI workloads to run where data lives, whether in the datacentre, multiple public clouds or at the edge. More than just the workloads, Red Hat’s vision for AI brings model training and tuning down this same path to better address limitations around data sovereignty, compliance and operational integrity. The consistency delivered by Red Hat’s platforms across these environments, no matter where they run, is crucial in keeping AI innovation flowing.
RHEL AI and the InstructLab community further deliver on this vision, breaking down many of the barriers to experimenting with and building AI models while providing the tools, data and concepts needed to fuel the next wave of intelligent workloads.
