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

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

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

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

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

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

A new compression technique from Google Research threatens to shrink the memory footprint of large AI models so dramatically ...

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

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

Big spending by big tech and an unexpected catalyst make the network specialist a buy.

The Google Research team developed TurboQuant to tackle bottlenecks in AI systems by using "extreme compression".

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.” [ ...