In recent years, foundation models have swept the computer vision field, facilitating the advancement of various tasks within different modalities. However, effectively designing an infrared foundation model remains an open question. In this paper, we introduce InfMAE, a foundation model tailored specifically for the infrared modality. Initially, we present Inf30, an infrared dataset developed to mitigate the scarcity of large-scale data for self-supervised learning within the infrared vision community. Moreover, considering the intrinsic characteristics of infrared images, we design an information-aware masking strategy. It allows for a greater emphasis on the regions with richer information in infrared images during the self-supervised learning process, which is conducive to learning strong representations. Additionally, to enhance generalization capabilities in downstream tasks, we employ a multi-scale encoder for latent representation learning. Finally, we develop an infrared decoder to reconstruct images. Extensive experiments show that our proposed method InfMAE outperforms other supervised and self-supervised learning methods in three key downstream tasks: infrared image semantic segmentation, object detection, and small target detection.