  Large Transformer models have achieved state-of-the-art results in neural
machine translation and have become standard in the field. In this work, we
look for the optimal combination of known techniques to optimize inference
speed without sacrificing translation quality. We conduct an empirical study
that stacks various approaches and demonstrates that combination of replacing
decoder self-attention with simplified recurrent units, adopting a deep encoder
and a shallow decoder architecture and multi-head attention pruning can achieve
up to 109% and 84% speedup on CPU and GPU respectively and reduce the number of
parameters by 25% while maintaining the same translation quality in terms of
BLEU.
