Google's TurboQuant compression tech cuts LLM memory use by 6x with no accuracy loss

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI chatbots. The cache grows as conversations lengthen, increasing both memory usage and power consumption. TurboQuant addresses this issue by reducing model size with “zero accuracy loss,” improving vector search efficiency, and…
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