Question

Your team is training a large Transformer-based language model from scratch on Vertex AI. The model is implemented in TensorFlow using primarily dense matrix multiplications and standard layers, with no custom C++ ops. Training on a single V100 GPU would take several weeks, and the dataset is hundreds of gigabytes of TFRecords stored in Cloud Storage. You need to minimize wall-clock training time while keeping cost reasonable. Which hardware choice is most appropriate?

Accepted Answer

Train on a Cloud TPU v3 Pod slice, using tf.distribute.TPUStrategy and sharding the TFRecord dataset across workers

Answer

Scale vertically to a single high-memory CPU machine with many vCPUs to avoid GPU quota limits

Answer

Use a single A100 GPU with a larger batch size, since multi-device training adds unnecessary complexity

Answer

Train on a cluster of preemptible CPU instances using parameter-server distribution to reduce cost