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

Google researchers have proposed TurboQuant, a method for compressing the key-value caches that large language models rely on ...

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 ...

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

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

Hundreds of popular add‑ons used encrypted, URL‑sized payloads to send search queries, referrers, and timestamps to outside servers, in some cases tied to data brokers and unknown operators. An ...

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 ...