I think ExLlama (and ExLlamaV2) is great and EXL2’s ability to quantize to arbitrary bpw, and its incredibly fast prefill processing I think generally makes it the best real-world choice for modern consumer GPUs, however, from testing on my workstations (5950X CPU and 3090/4090 GPUs) llama.cpp actually edges out ExLlamaV2 for inference speed (w/ a q4_0 beating out a 3.0bpw even) so I don’t think it’s quite so cut and dry.

For those looking for max batch=1 perf, I’d highly recommend people run their own benchmarks at home on their own system and see what works (also pay attention to prefill speeds if you often have long context)!

My benchmarks from a month or two ago: https://docs.google.com/spreadsheets/d/1kT4or6b0Fedd-W_jMwYpb63e1ZR3aePczz3zlbJW-Y4/edit#gid=1788227831

It depends on the use case. Each model may have their own strengths. I picked XWin and Airoboros as baseline 70B models for 2nd language conversational testing, and XWin outperformed (in human-evaled testing with a native speaker) a 70B model that had been pre-trained on an additional 100B tokens of said 2nd language. Shocking to say the least.