XDA Developers on MSN
TurboQuant tackles the hidden memory problem that's been limiting your local LLMs
A paper from Google could make local LLMs even easier to run.
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 ...
Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for ...
That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center ...
Tech Xplore on MSN
Holographic storage approach packs more data into the same space by encoding three properties of light
Researchers have developed a holographic data storage approach that stores and retrieves information in three dimensions by ...
Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...
Adam Hayes, Ph.D., CFA, is a financial writer with 15+ years Wall Street experience as a derivatives trader. Besides his extensive derivative trading expertise, Adam is an expert in economics and ...
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