Google's TurboQuant saves memory, but won't save us from DRAM-pricing hell

When Google unveiled TurboQuant, an AI data compression technology that promises to slash the amount of memory required to serve models, many hoped it would help with a memory shortage that has seen prices triple since last year. Not so much.

TurboQuant isn't the savior you might be hoping for. Having said that, the underlying technology is still worth a closer look as it has major implications for model devs and inference providers.

What the heck is TurboQuant

Detailed by Google researchers in a recent blog post, TurboQuant is essentially a method of compressing data used in generative AI from higher to lower precisions, an approach commonly referred to as quantization.

According to researchers, TurboQuant has the potential to cut memory consumption during inference by at least 6x, a bold claim at a time when DRAM and NAND prices are at record highs.

However, unlike most quantization methods, TurboQuant ...