I think you're confused about the benefits/drawbacks of increasing batch size. The training progress per optimizer step increases with batch size (otherwise the smallest batch would be always optimal). Increasing the batch size improves the quality of a single optimizer step but does not scale linearly. (i.e., larger batch training requires more samples to converge than small batch training even though fewer steps are required). Depending on the problem scale the computational efficiency of larger batches makes this tradeoff worth it because even though you need to process more samples to converge, it will take less wall clock time due to improved efficiency.