Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...

Quantization in neural network inference refers to the process of mapping high-precision parameters and activations to lower-precision representations, typically using integer or even binary values.

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 ...

You can now download Gemma 4 models with quantization-aware training to reduce the amount of mobile memory required to 1GB.

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...

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 ...

Compacting an AI model to run faster. AI quantization is primarily performed at the inference side (user side) so that it can run more quickly in phones and desktop computers. For example, whereas the ...

Google Quantum AI and Keysight joined forces to enhance Quantum circuit simulations with frequency-domain flux quantization Provides an extended library of quantum devices and a robust circuit design ...