Design the training infrastructure for a 70B dense mode...

Designing training infrastructure for a 70B model requires a rigorous calculation of the memory budget, accounting for parameters, gradients, and optimizer states. The choice of parallelism must be justified by the underlying hardware topology, typically utilizing tensor-parallelism within a node and pipeline-parallelism or ZeRO-3 across nodes to manage the scale.

Furthermore, the design must estimate the pipeline bubble overhead and clearly identify how batch size is consumed as a resource within the cluster. This approach ensures that the infrastructure is balanced between memory capacity and communication bandwidth.