The hardware and software that led to the revolution of deep learning was built during the era of computer vision. Differences in architecture and data between that domain and recommenders made the HW/SW stack a poor fit for deep learning based recommender systems, and the experience of many who explored recommendation on the GPU early on, myself included, was bad. In this talk we'll explore changes in GPU hardware within the last generation that make it much better suited to the recommendation problem, along with improvements on the software side that take advantage of optimizations only possible in the recommendation domain. A new era of faster ETL, Training and Inference is coming to the RecSys space and this talk will walk through some of the patterns of optimization that guide the tools we're building to make recommenders faster and easier to use on the GPU.