The ResNeXt architecture addresses the trade-off between nonlinearity and dimensionality in standard ResNet designs. Instead of increasing network depth or widening convolutions, ResNeXt increases the number of channels that carry information between blocks while avoiding a quadratic computational penalty. Inspired by the Inception block's strategy of separating information flow into independent groups, ResNeXt applies the exact same transformation across all of its parallel branches. This uniform multi-branch design minimizes the need for manual hyperparameter tuning for each branch.