Adarsh Mittal, a senior application-specific integrated circuit engineer, explores why many memory performance optimizations ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in ...

Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the model ...

Google's new TurboQuant algorithm drastically cuts AI model memory needs, impacting memory chip stocks like SK Hynix and Kioxia. This innovation targets the AI's 'memory' cache, compressing it ...

The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...

Google said this week that its research on a new compression method could reduce the amount of memory required to run large language models by six times. SK Hynix, Samsung and Micron shares fell as ...

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 ...

Micron is expected to report 148% revenue growth for the February quarter as average selling prices surge 32% quarter over quarter. The memory provider's stock has soared thanks to a shortage brought ...

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 ...