Running the model on consumer-grade Blackwell GPUs (due to native fp4 support).
Running on other hardware IS POSSIBLE. I lack conclusive data to say if it's a good idea.

Benchmarks

Measurement	Q4_K_S (imatrix)	MXFP4_MOE (default, 8 exps)	delta	MXFP4_MOE (16 exps)	delta	MXFP4_MOE (24 exps)	delta
pp512	3067.83	3567.72	+16.29%	2783.90	-9.26%	TBD	TBD
tg128	108	89.87	-16.79%	76.43	-29.23%	TBD	TBD
pp65536	1748.64	2219.20	+26.91%	1789.84	+2.36%	TBD	TBD
tg1024	87.81	88.97	+1.20%	67.38	-23.26%	TBD	TBD
pp65536 + tg1024 wall time (s)	49.14	41.03	-16.5%	51.81	+5.43%	TBD	TBD
hellaswag% (s=42, n=400, greedy)	56.5%	61.00%	+7.96%	64.00%	+13.27%	63.5%	+12.39%
hellaswag (s=42, n=400, greedy)	226	244	+18	256	+30	254	+28
winogrande% (n=1267, greedy)	59.51%	57.38%	-3.58%	64.17%	+7.83%	66.45%	+11.66%
winogrande (n=1267, greedy)	754	727	-27	813	+59	842	+88
Size (bytes)	15464827008	14727180640	-4.77%	14727180640	-4.77%	14727180640	-4.77%

tested on: --temp 0 -ngl 999 -b 512 -ub 512 -ctk q8_0 -ctv q8_0
pp65536/tg1024 test for Q4_K_S was run at -ngl 29 instead - it did not fit otherwise.
pp65536/tg1024 test for MXFP4_MOE(16 exps) was run at -ngl 30 instead - it did not fit otherwise.

Conclusion

MXFP4_MOE allows either Q4_K_S intelligence at -16.5% wall time, or higher than Q4_K_S intelligence at the cost of +5.43% wall time (with experts/tok override to 16), while being of smaller file size.
Is it worth it? I don't know. At this point, my particular config isn't compute-limited, but rather bottlenecked by PCI-E. WYSIWYG.

Disclosure

My only contribution is compute. This is not my merge. Have fun.

Cheers

Google - the base model.
Vortex5 - for the merge effort.
Everyone whose finetunes were included in the merge!

Koshkasa/Vortex5_G4-Midnight-Macaw-26B-A4B-MXFP4_MOE-GGUF

What's that?

Use case

Benchmarks

Conclusion

Disclosure

Cheers

Koshkasa/
Vortex5_G4-Midnight-Macaw-26B-A4B-MXFP4_MOE-GGUF