Retrieval-augmented generation (RAG) pipelines have become the backbone of modern AI applications, but scaling them comes at a cost. Storing 10 million float32 embeddings consumes 31 GB of RAM—a serious constraint for teams running local or on-premise inference. Enter Turbovec, an open-source vector index written in Rust with Python bindings that leverages Google Research’s TurboQuant algorithm. It slashes memory usage by 8x (to just 4 GB for the same corpus) and delivers search speeds that outpace FAISS IndexPQFastScan by 12–20% on ARM hardware. Below, we break down the ten essential details you need to know about this library, from its unique quantization approach to real-world performance numbers.
