Artificial Intelligence (AI) and Machine Learning (ML) architectures signify a transformative shift in the deployment of high-performance computing environments. Designing AI and ML clusters involves establishing a specific network infrastructure that revolves around low-latency and high-bandwidth links. This not only ensures optimal network performance but also streamlines deployment timelines.

At the heart of this architectural transformation is the significant impact of parallel processing capabilities, which act as the driving force behind the learning tasks fundamental to AI’s value proposition. Notably, AI clusters are making a transition from traditional CPU-based hardware to GPU-based nodes. This shift not only marks a change in computing paradigms but also poses a substantial challenge to established data center architectures, questioning current network design paradigms.

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