Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.

A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...

Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...

The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” [ ...

Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...

Investopedia contributors come from a range of backgrounds, and over 25 years there have been thousands of expert writers and editors who have contributed. Gordon Scott has been an active investor and ...

Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...

Spread the loveIn a groundbreaking development that has sent shockwaves through the tech industry, Google announced the launch of its new AI compression algorithm, TurboQuant. This innovative ...