Introduction: The Embedding Bottleneck and the Promise of Vector Quantization In the ever-evolving landscape of machine learning, the size and speed of models are paramount. Embedding tables, which map discrete data like words or user IDs to dense vector representations, are often a significant bottleneck, consuming vast amounts of memory and slowing down inference. Imagine