Inference vs. Training
Ron explains the crucial differences between inference and training workloads in machine learning. He highlights that inference is a scale-out problem, requiring low power and rapid deployment, while training is a scale-up task that benefits from high bandwidth communication across many interconnected servers. This distinction allows for tailored chip architectures that optimize performance for each workload.In this clip
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