Page 14 - Red Hat PR REPORT - MAY 2024

Red Hat Delivers Accessible, Open Source Generative AI
Innovation with Red Hat Enterprise Linux AI

 The offering is the first to deliver supported, indemnified and open source-licensed IBM Granite
LLMs under Red Hat’s flexible and proven enterprise subscription model
 Adds open source InstructLab model alignment tools to the world’s leading enterprise Linux
platform to simplify generative AI model experimentation and alignment tuning
 Provides a supported, enterprise-ready model runtime environment across AMD, Intel and
NVIDIA platforms for fueling AI innovation built on open source

MAY 13, 2024 – Red Hat, Inc., the world's leading provider of open source solutions, today
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 optimized, 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
stands to be further accelerated by an open approach to innovation.

Implementing an AI strategy requires more than simply selecting a model; technology
organizations 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