Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 ...
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.
Morning Overview on MSN
Google says TurboQuant cuts LLM KV-cache memory use 6x, boosts speed
Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in ...
Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...
Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...
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 ...
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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