Google’s TurboQuant Compression May Support Faster Inference, Same Accuracy on Less Capable Hardware
Google Research unveiled TurboQuant, a novel quantization algorithm that compresses large language models’ Key-Value caches ...
Researchers at North Carolina State University have developed a new AI-assisted tool that helps computer architects boost ...
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Self-working card trick using spelling principle
This video demonstrates a self-working card trick based on a mathematical spelling principle. By repeatedly spelling the name of a selected card, the deck cycles in a way that brings a predetermined ...
Working memory is like a mental chalkboard we use to store temporary information while executing other tasks. Scientists worked with more than 200 elementary students to test their working memory, ...
TL;DR: Google developed three AI compression algorithms-TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss-that reduce large language models' KV cache memory by at least six times without ...
Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.
If Google’s AI researchers had a sense of humor, they would have called TurboQuant, the new, ultra-efficient AI memory compression algorithm announced Tuesday, “Pied Piper” — or, at least that’s what ...
Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...
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