Large-scale generative recommenders, inspired by the success of Large Language Models (LLMs), face unique challenges in training and operational efficiency. Unlike LLMs, recommendation systems must process significantly larger catalogs—Netflix's model needs a catalog 40 times bigger than GPT-3’s. This results in high computational demands, with Netflix's model analyzing 2 trillion tokens against GPT-3’s 500 billion. To manage this overhead, techniques like compressed heads and sampled softmax are employed to reduce computational costs while maintaining performance. Understanding scaling laws specific to recommendation tasks is crucial. The research identifies novel scaling dynamics that differ from established models, guiding resource allocation for better personalization. The cold-start problem for new items is another significant hurdle, as large models require extensive training data to encode new entries effectively. To counter this, the approach includes multi-modal semantic towers that help infer properties from item content and metadata, improving recommendations for unseen items. Efficiency in training and inference distinguishes recommendation systems from typical LLMs. Frequent updates are necessary to reflect changing user preferences, leading to ongoing computational demands. Techniques such as mixed-precision training and gradient compression are standard, but the article emphasizes the importance of decoding efficiency. Large recommendation models often require vocabularies in the millions or billions, making decoding costs a major concern. The proposed sampled softmax method reduces vocabulary size during training, leading to a potential cost reduction of one to two orders of magnitude. Additionally, using projected heads to downsize embedding dimensions simplifies downstream integration for application teams.