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High-performance cluster networking for GPU systems has been traditionally associated with large-scale pre-training. As time passed, post-training and distributed inference applications brought their unique requirements as well. This talk surveys the training/inference parallelism-induced traffic patterns from training/inference of LLMs and the impact on evolution of scale-out and scale-up network.
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