The rapid growth of AI data—across training and inference workloads and all points in between—is increasing demand for high-performance compute infrastructure. Further, complex and data-intensive AI applications require hybrid multicloud connectivity to enable faster data transfers between critical workloads. Enterprises are struggling with outdated on-premises data centers that lack compute capacity and power, high-density cooling capabilities and the scalable infrastructure required to support AI workloads. They’re navigating a complex AI landscape driven by specific business needs and data residency, privacy and sovereignty considerations. While public clouds may be an option for hosting AI projects, concerns about privacy, vendor lock-in and…

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