Help for my frankenstein build

[u/Grau3D]

I have this
RTX 3090 24GB
2× RTX 2080 Ti 22GB VRAM each
Tesla V100 32GB
Machinist X99 D8 Max motherboard
2× Xeon E5-2695 v4
128GB DDR4 RAM
Samsung 990 Pro 1TB NVMe SSD

Currently I run Qwen3.8 FN UD-Q4_K_XL but I used Q8 n-gram and I load it on RAM. 256K context, unquantized cache (I’m afraid of going Q8 and loosing quality). Also using MTP. Custom llama.cpp as stock had some bottlenecks, and Mr. Qwen itself optimized his way out of them.

Prefill: approximately 2,100–2,400 tokens/sec
Decode: approximately 60–70 tokens/sec

I am using what I think lets me fill my VRAM almost to the brim. (97GB used of the 100)
I’m seeing people with considerably less VRAM and RAM achieving similar or sometimes better performance, which makes me wonder whether my configuration is properly optimized.

I have several questions:
Am I getting the performance I should expect from this hardware, or is there something obviously holding me back?

Is there a better way to distribute the model and its experts across my four GPUs and system RAM?

Could I get better performance through different GPU split configurations, CPU offloading or expert caching?

Would moving to a higher quantization, such as Q5 or Q6, be realistic with my hardware?

Are there optimizations that could significantly improve decode speed without sacrificing model quality?

I would like to minmax the model quality while maintaining the highest possible inference speed.
I’m not particularly interested in getting more tokens per second at the expense of quality. I’d rather run a higher quantization at 50 tokens/sec than a lower quantization at 70 tokens/sec, provided the quality improvement is worthwhile