Recent advancements in embedding quantization techniques have revolutionized the performance and efficiency of knowledge retrieval systems. Notably, the introduction of MAX Engine, paired with @trychroma for storing embeddings, has significantly enhanced the performance of compact, state-of-the-art (SOTA) embedding models like BGE, surpassing traditional frameworks such as PyTorch and ONNX runtime. Similarly, Cohere's Embed V3 Binary Vectors, when used with Vespa, offer a drastic reduction in vector database costs, achieving a 4x to 32x cost cut with just 128 bytes per vector. Furthermore, embedding quantization has been highlighted for its potential to achieve up to a 25x speedup in retrieval, a 32x reduction in memory usage, and a 4x reduction in disk space, while maintaining up to 99.3% of performance. This technique also promises up to a 45x faster retrieval and 96% accuracy on open Embedding Models, significantly impacting the scalability of RAG (Retrieval-Augmented Generation) applications.