Recommendation systems have high memory capacity and bandwidth requirements. Disaggregated memory is an upcoming technology that can improve memory utilization, increase memory capacity and bandwidth and allow independent compute and memory scaling. We investigate the use of disaggregated memory for recommenders and we posit that this new technology can provide significant benefits for recommenders’ training and inference.