This work proposes POMP, a prompt pre-training method for vision-language models. Being memory and computation efficient, POMP enables the learned prompt to condense semantic information for a rich set of visual concepts with over twenty-thousand classes. Once pre-trained, the prompt with a strong transferable ability can be directly plugged into a variety of visual recognition tasks including image classification, semantic segmentation, and object detection, to boost recognition performances in a zero-shot manner. Empirical evaluation shows that POMP achieves state-of-the-art performances on 21 datasets, eg, 67.0% average accuracy on 10 classification datasets (+ 3.1% compared to CoOp) and 84.4 hIoU on open-vocabulary Pascal VOC segmentation (+ 6.9 compared to ZSSeg).