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Google AI breakthrough means chatbots use six times less memory during conversations without compromising performance

Google reveals TurboQuant, a real-time memory compression method that cuts KV cache usage during inference by up to six times.
The memory savings come with maintained AI performance during conversations, according to Google.
PolarQuant reexpresses data from Cartesian to polar coordinates to enable tighter compression.
QJL adjusts vectors slightly during quantization to correct errors and maintain accuracy.
The breakthrough focuses on inference memory, where most AI memory savings apply.
The researchers tested TurboQuant on multiple AI models, including Llama 3.1-8B, Gemma, and Mistral AI.
Google unveiled TurboQuant at ICLR 2026 and will present PolarQuant and QJL at AISTATS 2026.
Experts say the memory efficiency gains could boost AI efficiency in search and other domains.
The news clarifies that training memory needs remain high, but inference memory could drop significantly.
The development signals potential wider adoption for memory-bound AI systems.

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