Machine-learning inference started out as a data-center activity, but tremendous effort is being put into inference at the edge. At this point, the “edge” is not a well-defined concept, and future ...

As AI workloads shift from centralized training to distributed inference, the network faces new demands around latency requirements, data sovereignty boundaries, model preferences, and power ...

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Recent industry trends, including the release of NVIDIA’s Rubin platform (developer.nvidia.com), point to a growing consensus that AI inference is reshaping data center architecture in a fundamental ...