We study a mismatch between the deep learning recommendation models’ flat architecture, common distributedtraining paradigm and hierarchical data center topology. To address the associated inefficiencies, we proposeDisaggregated Multi-Tower (DMT), a modeling technique that consists of (1) semantic-preserving tower transform(SPTT), a novel training paradigm that decomposes the monolithic global embedding lookup process into disjointtowers to exploit data center locality; (2) Tower Module (TM), a synergistic dense component attached to eachtower to reduce model complexity and communication volume through hierarchical feature interaction; and (3)Tower Partitioner (TP), a feature partitioner to systematically create towers with meaningful feature interactionsand load balanced assignments to preserve model quality and training throughput via learned embeddings. Weshow that DMT can achieve up to 1.9× speedup compared to the state-of-the-art baselines without losing accuracyacross multiple generations of hardware at large data center scales.