TorchRec provides common sparsity and parallelism primitives for large-scale recommendation systems. It enables training and inference of models with embedding tables sharded across many GPUs using hybrid data-parallelism and model-parallelism. Key features include automated sharding planners, pipelined training, and optimized kernels via FBGEMM for powering production recommender systems at scale.