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 ...

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 ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

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 ...

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