Compressing a large language model by 6x without losing any accuracy sounds like a trade-off that doesn’t exist. On March 24, 2026, Google Research published evidence that it does. The paper introduces TurboQuant, a vector quantization algorithm that targets one of the most expensive problems in running LLMs at scale:
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