Segmentation of 3D medical images is a labor-intensive task with important clinical applications. Recently, foundation models for image segmentation have received significant interest. Specifically, many works have proposed methods for the adaptation of promptable natural image foundation models to medical image segmentation. However, the shift to 3D volumes from 2D natural images has proven difficult, and many approaches have limited real-world clinical applicability due to large model sizes and corresponding heavy computational requirements. Here, we present an original model for generalized, promptable 3D medical image segmentation. Our approach leverages a lightweight convolutional backbone while simultaneously integrating information from single-point prompts at multiple spatial resolutions. Our approach dramatically reduces the computational burden for promptable segmentation while also outperforming similar recent works on a diverse dataset of 98,699 image-mask pairs from CT and MRI datasets.