[AnthonyMouse]

> Why is there not a greater focus on quantization to optimize model performance, given the evident need for more GPU resources?

There is an inherent trade off between model size and quality. Quantization reduces model size at the expense of quality. Sometimes it's a better way to do that than reducing the number of parameters, but it's still fundamentally the same trade off. You can't make the highest quality model use the smallest amount of memory. It's information theory, not sorcery.

[netdur]

Yes Quantization compresses float32 values to int8 by mapping the large range of floats to a smaller integer range using a scale factor. This scale factor is key for converting back to floats (dequantization), aiming to preserve as much information as possible within the int8 limits. While quantization reduces model size and speeds up computation, it trades off some accuracy due to the compression. It's a balance between efficiency and model quality, not a magic solution to shrink models without losing some performance.

Quantization is essential for me since a 7B model won't fit on my RTX 2060 with only 6GB of VRAM. It allows me to compress the model so it can run on my hardware.