How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.In this work, we present an efficient and affordable post-training quantization approach to compress large Transformer-based models, termed as \OURS.

The accuracy drop varies a lot under different benchmarks and scenarios.

How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.

How to efficiently serve ever-larger trained natural language models in practice has become exceptionally challenging even for powerful cloud servers due to their prohibitive memory/computation requirements.In this work, we present an efficient and affordable post-training quantization approach to compress large Transformer-based models, termed as \OURS.