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2/21/25, 3:36 PM               AI Freedom of Choice with Red Hat - Middle East Business News and Information - mid-east.info
        AI Freedom of Choice with Red Hat


        February 20, 2025                                                                                           12

        Red Hat collaborates with NVIDIA, Lenovo, Microsoft, AWS, Dell, AMD and Intel

                                                        Advertise here.




        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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