QBit is a new data type added to ClickHouse to enhance vector search performance by storing floats as bit planes, allowing users to adjust precision and speed at query time without altering the original data. This innovation addresses the limitations of traditional vector search methods, such as brute force and Approximate Nearest Neighbour (ANN) techniques like HNSW, which often require upfront parameter decisions affecting recall and performance. QBit enables dynamic exploration of the balance between accuracy and speed by reading only the required subcolumns to reconstruct data to the user-specified precision, thus reducing I/O and computation time while maintaining high accuracy. This flexibility is particularly beneficial in vector search applications, such as music recommendations and retrieval-augmented generation (RAG) for large language models, where traditional vector stores are not always ideal for handling structured data. Through examples and benchmark tests, QBit has demonstrated its ability to significantly speed up vector searches without compromising accuracy, even when float values are reduced to as few as five bits.