How does TensorFlow handle large embedding layers efficiently?

TensorFlow uses sparse updates and partitioning to handle large embeddings efficiently.

Embedding layers can become huge in recommendation systems. TensorFlow optimizes them using sparse gradient updates, sharding across devices, and lookup tables instead of dense matrices. This reduces memory and computation overhead.