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

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

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

Google developed a new compression algorithm that will reduce the memory needed for AI models. If this breakthrough performs ...

Memory stocks continued to struggle in early trading Tuesday amid fears over Google's AI compression algorithm.

Micron Technology (NASDAQ:MU | MU Price Prediction) shares are up 9% in Wednesday morning trading, climbing from an opening ...

With TurboQuant, Google promises 'massive compression for large language models.' ...

That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center ...

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 could cut LLM memory use sixfold, signaling a shift from brute-force scaling to efficiency and broader AI ...

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

A more efficient method for using memory in AI systems could increase overall memory demand, especially in the long term.