As AI workloads move from training to real-world inference, Arrcus CEO says, network fabrics must evolve to keep up with the demands.

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 ...

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 ...