Hyperconverged infrastructure (HCI) revolutionized data center design by promising a streamlined, scalable building block for modern workloads. Yet, as enterprises push these integrated systems to handle the massive, unpredictable data flows of AI training and real-time analytics, a critical weakness is being exposed: the network. The very convergence that simplifies management can create a single, over-subscribed pipeline that must carry everything—storage traffic, VM migration, user access, and the explosive east-west communication of distributed AI jobs. When a single node’s SSD can output data faster than the network can move it, or when a GPU cluster sits idle waiting for model parameters to synchronize across a congested backplane, the promise of HCI turns into a performance prison. This video explores how network bottlenecks are emerging as the primary constraint in HCI environments, crippling the scalability of data-intensive workloads, and examines the architectural shifts—from RDMA and dedicated storage fabrics to disaggregated designs—that are necessary to set AI and analytics free.
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