Need support for llama.cpp with multi GPU

Using llama.cpp I seem to be unable to get my to GPUs working tougether correclty, so I need help somehow.

Setup: 96GB RAM, one Blackwell 5000 (48GB) and one 3090 (24GB).

I am trying to run the UD-Q3_K_XL quant of Deepseek4 flash which has about 120GB size. Using just the Blackwell I would put most of the experts on the system RAM. I would have thought that putting some experts on the 3090 I would have thought to be faster (or at least not slower) than putting the experts just on the RAM.

What I have tried:

• for all trails I try run with these common flags: --temp 1.0 --top-p 0.95 --min-p 0 -ngl 99 -ctk bf16 -ctv bf16 --jinja --flash-attn on --no-mmap
• baseline, using just the Blackwell (-dev CUDA0 --fit on --n-cpu-moe 32): full context and a generation speed of 16t/s
• that above but swapping "-dev CUDA0" with "--split-mode layer": fit fails trying to allocate 31gig on the 3090
• adding -ts 2,1 results in exactly the same as above
• back to the drawing board, start with minimal setup using just the args from my first bullet point above): fit settles at 4096 context size
• trying to fix the context by setting -c 1048576 or fitc 1048576: fit now fucks up and just pushes 25 gigs on the blackwell and just 6 gigs on the 3090

Now I moved to manually setting the layers:

• agian the baseline with just the balckwell (-dev CUDA0 -ot "(1[2-9]|[2-9][0-9]).ffn_.*_exps.*=CPU"): full context, same 16t/s speed as with moe-cpu
• just as above but without -dev CUDA0: speed drops to 11t/s
• manually puting experts on 3090 (-ot "(1[2-7]).ffn_.*_exps.*=CUDA1" -ot "(1[8-9]|[2-9][0-9]).ffn_.*_exps.*=CPU"): speed of 12t/s

What am I doing wrong?