India, March 31 -- For years, the AI story has been predictable. Build bigger models, add more compute, and accept rising costs as part of progress. Google TurboQuant AI challenges that thinking by focusing on something less visible but far more limiting, memory. Instead of scaling up infrastructure, it reduces the memory footprint of large language models by more than six times while preserving full accuracy.

This development signals a deeper shift across the AI ecosystem. Efficiency is no longer a secondary goal. It is becoming central to how systems are designed, deployed, and scaled in real-world environments.

Google TurboQuant AI targets the KV cache, a core component that allows models to remember and reuse context during inferenc...