Google’s TurboQuant cuts AI memory use without losing accuracy
Large language models carry a persistent scaling problem. As context windows grow, the memory required to store key-value (KV) caches expands proportionally, consuming GPU memory and slowing inference. A team at Google Research has developed three compression algorithms: TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss (QJL). All three are designed to compress those caches aggressively without degrading model output quality. The overhead problem in vector quantization Vector quantization has long been used to compress the high-dimensional numerical … More
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