Low-dose computed tomography (LDCT) scans reduce the radiation dose of computed tomography (CT) scans but come at the expense of image quality. Deep-learning (DL) image denoising techniques can enhance these LDCT images to match the quality of their regular-dose CT counterparts. To achieve better denoising performance than the current state of the art, we present a novel 3D DL architecture for LDCT image denoising called 3D-DDnet. The architecture leverages the inter-slice correlation in volumetric CT scans to obtain better denoising performance and employs distributed data parallel (DDP) strategies along with transfer learning to achieve faster training. The DDP training strategy enables a scalable multi-GPU approach on Nvidia A100 GPUs, which allows the training of previously prohibitively large volumetric samples. Our results show that 3D-DDnet achieves \(10\%\) better mean square error (MSE) on LDCT scans than its 2D predecessor (i.e., 2D-DDnet). In addition, the transfer learning in 3D-DDnet leverages existing trained 2D models to “jump start” the weights and biases of our 3D DL model and reduces training time by \(50\%\) while maintaining accuracy.