Tether successfully integrated Google’s TurboQuant into the inference engine of its local AI framework, QVAC. It is the ...

I'll study TurboQuant after I watch the soccer match. Just as I was thinking that, Openfang picked up some incredible news. An evolved version of TurboQuant. I've already loaded the repository into ...

TurboQuant is a compression algorithm introduced by Google Research (Zandieh et al.) at ICLR 2026 that solves the primary memory bottleneck in large language model inference: the key-value (KV) cache.

Embeddings, or the numerical representation of heterogeneous content, are the basis of vector search workloads. However, the sizes of embeddings make them hard to scale and expensive to process.

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