Google's TurboQuant algorithm can cut AI memory needs by 6x, having the potential to fix the global RAM crisis and change the ...

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

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 LLC has unveiled a technology called TurboQuant that can speed up artificial intelligence models and lower their ...

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

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

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

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

Editor’s note: This work is part of AI Watchdog, The Atlantic’s ongoing investigation into the generative-AI industry. On Tuesday, researchers at Stanford and Yale revealed something that AI companies ...

A Ruby port of lz-string - a string compression algorithm with support for multiple encodings (base64, URI, UTF16) and seamless JavaScript interoperability ...