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 ...

Google's TurboQuant storage algorithm is expected to reduce the memory requirements for AI workloads. Shares of Sandisk have dropped on this news, opening a solid buying opportunity for investors.

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

Google has quietly made its AI Pro plan even more useful. The company has increased the bundled storage from 2TB to 5TB without changing the monthly price. This means that users already paying around ...

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.

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’s TurboQuant is making waves in the AI hardware sector by addressing long-standing challenges in memory usage and processing efficiency. Developed with components like the Quantized ...

Google is sweetening the deal for those who might want to upgrade their plans to use more AI features. The company's mid-tier AI Pro plan, which costs $20 a month, now includes 5TB of cloud storage ...

Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...

Google has unveiled TurboQuant, a new AI compression algorithm that can reduce the RAM requirements for large language models by 6x. By optimizing how AI stores data through a method called ...

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 ...