You are pre-training a large language model on Google Cloud. This model includes custom TensorFlow operations in the training loop. Model training will use a large batch size, and you expect training

While A2-series GPU machines are powerful, a TPU Pod slice is generally more cost-effective and performant for week-long, very large language model pre-training with custom TensorFlow operations.

TPU Pod slices are specifically designed for large-scale machine learning workloads, especially large language models, offering excellent performance and cost efficiency for long-running training jobs. The tf.distribute.TPUStrategy is the correct distribution strategy for TensorFlow models leveraging TPUs, and a v4-l28 accelerator type provides significant compute power.

C2D-highcpu machines are CPU-only instances, which are highly inefficient and cost-prohibitive for training large language models that require accelerators.

A2-highgpu-8g machines are powerful, but TPU Pod slices are often preferred for the scale and duration of pre-training large language models, offering better performance and cost optimization.