We introduce SPFSplat, an efficient framework for 3D Gaussian Splatting from sparse multi-view images, requiring no ground-truth poses during both training and inference. Our method simultaneously predicts Gaussians and camera poses from unposed images in a canonical space within a single feed-forward step. During training, the pose head estimate the poses at target views, which are supervised through the image rendering loss. Additionally, a reprojection loss is introduced to ensure alignment between Gaussians and the estimated poses of input views, reinforcing geometric consistency. This pose-free training paradigm and efficient one-step feed-forward inference makes SPFSplat well-suited for practical applications. Despite the absence of pose supervision, our self-supervised SPFSplat achieves state-of-the-art performance in novel view synthesis, even under significant viewpoint changes. Furthermore, it surpasses recent methods trained with geometry priors in relative pose estimation, demonstrating its effectiveness in both 3D scene reconstruction and camera pose learning.