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

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

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

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

Researchers have developed a holographic data storage approach that stores and retrieves information in three dimensions by ...

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 ...

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.” [ ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

Over the past decades, computer scientists have introduced numerous artificial intelligence (AI) systems designed to emulate the organization and functioning of networks of neurons in the brain.