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On Tuesday, Eastern Time, Google unveiled TurboQuant, an ultra-efficient AI memory compression algorithm that reduces cache memory usage by at least sixfold and enhances performance by up to eightfold when running large language models—all without sacrificing accuracy. This allows artificial intelligence to remember more information while occupying less memory space. The algorithm achieves high-quality compression and error elimination through two key steps, PolarQuant and QJL, without requiring model retraining or fine-tuning. Tests on open-source models by Google show that TurboQuant achieves approximately sixfold key-value cache memory compression and up to eightfold performance improvement on H100 GPU accelerators. Google plans to present its research findings at the ICLR 2026 conference next month.
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