A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

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 ...

Memory prices are plunging and stocks in memory companies are collapsing following news from Google Research of a ...

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...

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

Memory stocks continued to struggle in early trading Tuesday amid fears over Google's AI compression algorithm.

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

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression algorithm that’s going viral over ...

With TurboQuant, Google promises 'massive compression for large language models.' ...

Google LLC has unveiled a technology called TurboQuant that can speed up artificial intelligence models and lower their ...

Big spending by big tech and an unexpected catalyst make the network specialist a buy.

Google's TurboQuant algorithm is going to be a boon for the memory industry, setting these three stocks up for outstanding ...