Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

A paper from Google could make local LLMs even easier to run.

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

Google researchers have proposed TurboQuant, a two-stage quantization method that, according to a recent arXiv preprint, can ...

PrismML's approach is based on work done by Caltech electrical engineering professor Babak Hassibi and colleagues. The ...

One of the most widely used techniques to make AI models more efficient, quantization, has limits — and the industry could be fast approaching them. In the context of AI, quantization refers to ...

The general definition of quantization states that it is the process of mapping continuous infinite values to a smaller set of discrete finite values. In this blog, we will talk about quantization in ...