Scaling AI Isn’t A Computing Problem—It's A Network Problem

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Scaling AI Isn’t A Computing Problem—It's A Network Problem
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Serge Lucio is the VP and GM of Agile Operations Division, Broadcom Inc. Read Serge Lucio's full executive profile here.

AI models are rapidly increasing in complexity, demanding more powerful computing resources for effective training and inference. This trend has sparked significant interest in scaling computational capacity for AI, with teams exploring new hardware architectures and distributed computing strategies to extend the boundaries of what AI can achieve.In the 1960s, a cofounder of Intel predicted that the number of transistors on a microchip would double about every two years.

As AI continues to advance, the true bottleneck often lies in the network. When AI data and workloads are distributed across numerous nodes, robust networking solutions can deliver the high bandwidth and low latency required. Innovations in networking technologies, such as high-speed interconnects and optimized communication protocols, can support the scale and speed necessary for modern AI applications.

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