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5/14/24, 2:32 PM     Red Hat Delivers Accessible, Open Source Generative AI Innovation with Red Hat Enterprise Linux AI - Middle East News 247

        Red Hat Delivers Accessible, Open Source


        Generative AI Innovation with Red Hat Enterprise


        Linux AI



        By Middle East News 247     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:











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