Google TurboQuant: Cut LLM Inference Memory 6x Without Retraining
Presented at ICLR 2026 on April 25, Google's TurboQuant compresses the KV-cache of large language models to 3 bits with no retraining and no measurable quality loss. The practical result is a 6x reduction in inference memory, which directly lowers the cost of running agentic AI workflows at scale. If you self-host models or pay per-token for long-context tasks, evaluate TurboQuant as an immediate cost lever.