Running a large language model is expensive, and a surprising amount of that cost comes down to memory, not computation.

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.

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 ...

Highflying memory stocks like Micron and SanDisk have been dented this week and it might have something to do with TurboQuant, a compression algorithm detailed by Google in a research paper this week.

At its core, the TurboQuant algorithm minimizes the space required to store memory while also preserving model accuracy. To ...

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.” ...

Google sent shockwaves through a small corner of the artificial intelligence (AI) market when it released new research that could significantly impact certain chipmakers. The Alphabet (NASDAQ: GOOG) ...

Google Research's TurboQuant memory-compression algorithm has raised concerns that demand for AI-related memory could weaken, but South Korean experts and analysts say the market reaction may be ...