SK Hynix, Samsung and Micron shares fell as investors fear fewer memory chips may be required in the future.

A more efficient method for using memory in AI systems could increase overall memory demand, especially in the long term.

Google unveils TurboQuant, PolarQuant and more to cut LLM/vector search memory use, pressuring MU, WDC, STX & SNDK.

Memory stocks fell Wednesday despite broader technology sector strength, with shares dropping after Google unveiled ...

Alphabet's TurboQuant technology could reduce AI memory needs by six times, the company said. Memory chip stocks fell sharply ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

BingoCGN, a scalable and efficient graph neural network accelerator that enables inference of real-time, large-scale graphs through graph partitioning, has been developed by researchers at the ...

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.” [ ...