An optical flow estimation network suitable for edge GPU devices was proposed, aiming to solve the problem that dense optical flow estimation was difficult to deploy on embedded systems due to huge quantity of computation. Firstly, to fully exploit the GPU resources, an efficient feature extraction network was designed to reduce memory access costs. Secondly, by adopting a flat-shaped iterative update module to estimate the optical flow, the size of the model was further reduced, and the utilization of GPU bandwidth was improved. Experimental results on different datasets show that the proposed model has efficient inference capability and excellent flow estimation performance. In particular, compared with the advanced lightweight models, the proposed model reduces the error by 12.8% with only 0.54 Mb parameters, and improves the inference speed by 22.2%, demonstrating the satisfactory performance on embedded development boards.