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ERASE introduces a novel training schedule that detaches subgraphs to overlap backward passes with forward work, improving throughput by up to 9.51% in large-scale recommendation systems while preserving model performance.
This article explains how to implement asynchronous continuous batching for LLM inference, overlapping CPU batch preparation with GPU computation to maximize utilization and reduce idle time.