As companies progress through the various stages of AI maturity, they continuously discover new infrastructure requirements. One such requirement is transforming their networking infrastructure to run AI workloads on GPUs. Given their significant investment in acquiring and managing GPUs, companies need to ensure these servers are constantly running—without connectivity interruptions, latency challenges or bandwidth issues. Traditionally, Ethernet has been the go-to choice for CPU networking. However, the high-performance computing demands of processing AI workloads across large, distributed GPU clusters have raised the bar for performance, scalability and efficiency. Applications such as natural language processing, computer vision, advanced driver-assistance systems (ADAS),…

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