Turbocharge Your Agent's Retrieval with TurboQuant - Shashi Jagtap, Superagentic AI

This talk introduces TurboQuant, a training-free compression method from Google Research that reduces embedding memory footprint by 5x (from 32-bit to 3-4 bits) without losing search quality. It details how TurboQuant works through scalar quantization and a crucial one-bit error correction step, QJL, enabling agents to remember more on existing hardware by optimizing both KV cache and RAG vector stores. A live demo showcases its effectiveness, making it a vendor-neutral solution for efficient AI agent retrieval.