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2/21/25, 3:35 PM                                    AI Freedom Of Choice With Red Hat




        AI Freedom Of Choice With Red Hat                                                       Date
                                                                                                2025-02-20 06:17:11


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        (MENAFN- Mid-East Info) Red Hat collaborates with NVIDIA, Lenovo, Microsoft, AWS, Dell, AMD and Intel
        Red Hat continues to build upon its long-standing collaborations with major IT players to help customers implement
        and extend AI innovation across hybrid cloud environments. Red Hat Enterprise Linux AI (RHEL AI) and Red Hat
        OpenShift AI now offer a supported, optimised experience with a wide range of GPU-enabled hardware and software
        offerings from NVIDIA, Lenovo, Dell, Microsoft, AWS, Intel and others.
        Artificial intelligence (AI) model training requires optimized hardware and powerful computation capabilities. AI
        platforms must also support a broad choice of accelerated compute architectures and GPUs. Customers can get
        more from RHEL AI and Red Hat OpenShift AI by extending it with other integrated services and products announced
        in the last eight months:












        RHEL AI on Lenovo ThinkSystem SR675 V3 servers
        RHEL AI on Dell PowerEdge
        RHEL AI support for AMD Instinct Accelerators
        RHEL AI on Microsoft Azure
        RHEL AI and Red Hat OpenShift AI on AWS
        Red Hat OpenShift AI with Intel Gaudi AI and Intel Xeon and Core processors
        Red Hat OpenShift with NVIDIA AI Enterprise
        The Cloud is Hybrid. So is AI.
        For 30 years, open source has been a driving force behind innovation. Red Hat has played a key role in this
        evolution, first with enterprise-grade Linux (RHEL) in the 2000s and later with Red Hat OpenShift for containers and
        Kubernetes. Today, Red Hat continues this journey with AI in the hybrid cloud.
        AI models often need to run as close to an organization's data as possible to reduce latency and improve efficiency.
        This requires models to be supported wherever needed, from the datacenter to public clouds to the edge. AI platforms
        must be able to stretch across all of these footprints, seamlessly and without integration challenges. AI is the ultimate
        hybrid workload.

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