Pre-training large language models faces significant memory challenges due to the large size of model weights.We propose STaged parameter-Efficient Pre-training (STEP), which combines ideas from parameter-efficient tuning and staged training. We conduct experiments on pre-training models of various sizes and demonstrate that STEP can achieve up to a 40.4% reduction in maximum memory requirement compared to vanilla pre-training while maintaining comparable performance.