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 ...

Vector quantization, stock dips & RAMageddon – the real story behind how a memory compression algorithm went viral ...

Long context AI no longer means using data centres thanks to Tether acting swiftly on groundbreaking research from Google.

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.” ...

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 ...

This voice experience is generated by AI. Learn more. This voice experience is generated by AI. Learn more. I have written in March about Google’s TurboQuant for compressing data in memory for AI ...

Lossless compression is used for applications where the original data must be fully restored following decompression. Examples of applications requiring lossless compression include network data, ...

With people on the internet insisting that M1 Macs run well with minimal RAM (and the standard configs being minimal), I was wondering if anyone has a detailed explanation on how memory compression on ...

[Dominic Szablewski] was tinkering around with compressing RGB images, when he stumbled upon idea of how to make a simple lossless compression algorithm, resulting in the Quite OK Image Format, which ...

I have written in March about Google's TurboQuant for compressing data in memory for AI applications, focusing on data center applications. In that article, I said that TurboQuant is a compression ...