Medical image segmentation plays a crucial role in clinical practice, aiding in identifying tumors, delineating organs, and monitoring disease progression. The advent of the Segment Anything Model (SAM) has enabled the development of universal medical image segmentation models that generalize across different modalities. However, the accessibility of such deep learning models in clinical settings is still limited by the reliance on powerful computing devices. In this paper, we propose MedficientSAM, which adopts the EfficientViT model to replace the heavy image encoder in SAM and then distills the knowledge from the MedSAM model on the challenge’s training set. To further improve inference time, we re-implement the inference pipeline in the C++ programming language, optimizing the runtime on edge devices. MedficientSAM outperforms MedSAM in both accuracy and efficiency, achieving average DSC and NSD scores of 0.8642 and 0.8795, respectively, on the public validation set. The average inference time is 1.0083 s for 2D images and 8.9585 s for 3D images. Our code and models are publicly available at https://github.com/hieplpvip/medficientsam .