Faster than Light: Optimizing Generative Recommender Training Efficiency at LinkedIn

This post details engineering techniques to optimize the training efficiency of Generative Recommenders (GRs) at LinkedIn, focusing on data pipeline, compute, and training lifecycle optimizations. Optimizations include a C++-based fused data loader, improved attention kernels (FlashAttention-3, FlexAttention), fused metrics calculation, fused optimizer, and fused embedding table lookups. These optimizations reduced end-to-end GPU hours by up to 65% in internal production training workloads.