Deep Learning Recommendation Models (DLRMs) are central to enhancing user engagement and experience with internet and ecommerce companies.DLRMs provide content and commercial suggestions by modeling user behavior.DLRMs rely on embedding tables to capture the user behavior, where users with similar interests may be represented closer in the embedding space.Embedding tables scale to tens of terabytes as the number of users and features grows, presenting challenges in training and storage.These models typically require substantial GPU memory, as embedding operations are not compute-intensive but occupy significant storage.While some solutions have explored offloading embedding tables to CPU, this approach still demands terabytes of memory and places a significant burden on CPU-GPU interconnect.We introduce LEAF, a multi-level hashing framework that compresses the large embedding tables based on real-time access frequency distribution.In particular, LEAF leverages a streaming algorithm to estimate access distributions on the fly without relying on model gradients or requiring a priori knowledge of access distribution.By using multiple hash functions, LEAF minimizes the collision rates of feature instances.Experiments show that LEAF outperforms stateof-the-art compression methods on Criteo Kaggle, Avazu, KDD12, and Criteo Terabyte datasets, with testing AUC improvements of 1.411%, 1.885%, 2.761%, and 1.243%, respectively.The source code of LEAF is available at github.com/chaoyij/LEAF.