  Since hardware resources are limited, the objective of training deep learning
models is typically to maximize accuracy subject to the time and memory
constraints of training and inference. We study the impact of model size in
this setting, focusing on Transformer models for NLP tasks that are limited by
compute: self-supervised pretraining and high-resource machine translation. We
first show that even though smaller Transformer models execute faster per
iteration, wider and deeper models converge in significantly fewer steps.
Moreover, this acceleration in convergence typically outpaces the additional
computational overhead of using larger models. Therefore, the most
compute-efficient training strategy is to counterintuitively train extremely
large models but stop after a small number of iterations.
  This leads to an apparent trade-off between the training efficiency of large
Transformer models and the inference efficiency of small Transformer models.
However, we show that large models are more robust to compression techniques
such as quantization and pruning than small models. Consequently, one can get
the best of both worlds: heavily compressed, large models achieve higher
accuracy than lightly compressed, small models.
