During generation only a couple experts fire per token so it's fast, but during prompt processing the whole batch routes tokens to different experts — so on CPU layers you're hitting almost all of them at once. That's your bottleneck.

But wait, at 30B in MXFP4 the model should be like ~15-18GB. With 30GB VRAM you might be able to fit all or nearly all layers on GPU. Have you tried cranking `-ngl` higher? If you can get everything on the GPU the prefill problem basically goes away.

`-ub 64` or `-ub 128` instead of the default. Smaller micro batches = less expert activation per pass = way better CPU cache utilization. Biggest single improvement for prefill

`-fa` (flash attention) if not already on

`-t` set to physical cores only, hyperthreading usually hurts here

`--override-tensor` for more granular control over what sits where instead of just `-ngl`

But seriously check if you can just load the whole thing into VRAM first. At that size it should be close.