Recently, pre-trained language models based on the Transformer structure like BERT and RoBERTa have achieved remarkable results on various natural language processing tasks and even some computer vision tasks. However, these models have many parameters, hindering their deployment on edge devices with limited storage. In this talk, I will first introduce some basics about pre-trained language modeling and our proposed pre-trained language model NEZHA. Then I will elaborate on how we alleviate the concerns in various deployment scenarios during the inference and training period. Specifically, compression and acceleration methods using knowledge distillation, dynamic networks, and network quantization will be discussed. Finally, I will also discuss some recent progress about training deep networks on edge through quantization.