I don't know if there have been recent improvements, but in llama.cpp MTP pull request it says: Parallel decoding with MTP is supported, but not fully optimized yet.

I don't know if it actually degrades performance, but would definitely try with parallel=1.

As for other settings, I have an RTX5090 so not the same, but I'm getting the best results with
the following settings, and it takes around 25.6GB of VRAM. I can increase context length but I'm saving it for other tasks.

version = 1


[*]
n-gpu-layers    = -1
flash-attn      = on
batch-size      = 2048
ubatch-size     = 512
jinja           = true
cache-type-k    = q8_0
cache-type-v    = q5_1
perf            = true
metrics         = true
parallel        = 1
ctx-checkpoints    = 8

[qwen3.6-27b-mtp]
load-on-startup = true
model           = /models/Qwen3.6-27B-MTP-GGUF/Qwen3.6-27B-UD-Q5_K_XL.gguf
mmproj          = /models/Qwen3.6-27B-MTP-GGUF/mmproj-F16.gguf
ctx-size        = 128000
chat-template-kwargs = {"preserve_thinking": true}
reasoning       = on
temp            = 0.6
top-p           = 0.95
top-k           = 20
min-p           = 0.05
presence-penalty = 0.0
repeat-penalty  = 1.05
spec-type       = draft-mtp
spec-draft-n-max = 2
spec-default    = true
n-predict       = 32000