You are tasked with pre-training a large language model on Google Cloud. The model includes custom TensorFlow operations within the training loop, and the training process will use a large batch size. This pre-training process is anticipated to take several weeks, so it is crucial to configure a training architecture that minimizes both the training time and compute costs. Considering the available options for distributed strategies and hardware, what approach should you take?