Future frame prediction is a challenging task in the deep learning field due to its inherent uncertainty and complex spatiotemporal dynamics. Significant improvement in prediction accuracy has been achieved by state-of-the-art methods at the expense of complex, computationally intensive deep neural networks, which makes it difficult to deploy in mobile devices. Hence, we propose a lightweight model using 3D separable convolutions, which can predict future video frames with reasonable accuracy-complexity tradeoffs. Experimental studies on three popular video prediction datasets demonstrate that compared to state-of-the-art methods, our proposed method significantly reduces the model size and computational complexity with tolerable accuracy loss.