You work for a rapidly growing social media company.

TPU VMs with TPUv3 Pod slices are the most scalable and performant option for training large-scale recommender models on Google Cloud. TPUv3 Pods can provide up to 2048 cores and 32 TB of memory, and can process billions of examples and features in minutes. The TPUEmbedding API is designed to efficiently handle large-scale categorical features and embeddings, and can reduce the memory footprint and communication overhead of the model. The other options are either less scalable (B and C) or less efficient (D) for this use case.