I won’t use anything else for GPU processing.

The quality bump I’ve seen for my 4090 is very noticeable in speed, coherence and context.

Wild to me that thebloke doesn’t ever use it.

Easy enough to find quants though if you just go to models and search “exl2” and sort by whatever.

In addition to what others said, exl2 is very sensitive to the quantization dataset, which it uses to choose where to assign those “variable” bits.

Most online quants use wikitext. But I believe if you quantize models yourself on your own chats, you can get better results, especially below 4bpw.

EXL2 provides more options and has a smaller quality decrease for as far as I know.

EXL2 runs fast and the quantization process implements some fancy logic behind the scenes to do something similar to k_m quants for GGUF models. Instead of quantizing every slice of the model to the same bits per weight (bpw), it determines which slices are more important and uses a higher bpw for those slices and a lower bpw for the less-important slices where the effects of quantization won’t matter as much. The result is the average bits per weight across all the layers works out to be what you specified, say 4.0 bits per weight, but the performance hit to the model is less severe than its level of quantization would suggest because the important layers are maybe 5.0 bpw or 5.5 bpw, something like that.

In short, EXL2 quants tend to punch above their weight class due to some fancy logic going on behind the scenes.