Turbocharge Your Agent's Retrieval with TurboQuant - Shashi Jagtap, Superagentic AI
TurboQuant compresses agent retrieval embeddings to 3-4 bits, cutting memory cost 5x without degrading search quality.
“Today, we will see how you can cut memory cost of agent retrieval five times without breaking your search.”
A founder presents TurboQuant, a compression algorithm (based on a Google Research ICLR 2026 paper combining PolarQuant and QJL) that stores embeddings and KV cache in 3-4 bits instead of 32, cutting retrieval memory roughly 5x. It targets local/on-device agent deployments where KV cache and vector indexes compete for RAM, but it is a niche engineering optimization rather than a major industry signal.