Your setup is definitely a monster compared to mine so it should give much better results. I also believe you run dense models with decent results with that setup.

Not sure what llamacpp build you are using but on mine using this fork: https://github.com/TheTom/llama-cpp-turboquant on commit 407f3237bfb3eeaff61546797de3d8c1a96be748 compiled for Pascal, I am able to push my current system to 131k context with minimal loss in quality. For reference this is my config.ini file for this model (Cyber is the better uncensored version btw):

[*] # Global defaults jinja = 1 metrics = 1 threads = 12 threads-batch = 12 flash-attn = on sleep-idle-seconds = 99999 timeout = 99999 no-mmap = 1 mlock = 1 ctk = turbo4 ctv = turbo2

[qwen36-a3b] model = /root/llm/models/Cyber-Tiel-Coder-35B-A3B-MTP-UD-IQ4_XS.gguf alias = qwen36-a3b n-cpu-moe = 32 temp = 0.6 top-p = 0.95 top-k = 20 min-p = 0.0 presence-penalty = 0.0 repeat-penalty = 1.0 reasoning = on n-predict = 32768 reasoning-format = deepseek reasoning-budget = 16384 reasoning-budget-message = "Proceed to final answer" reasoning-preserve = true swa-full = true np = 2 batch-size = 4096 ubatch-size = 2048 cache-reuse = 256 ngl = auto ctx-checkpoints = 33 ctx-size = 262144 no-mmproj = true no-mmproj-offload = true spec-type = draft-mtp spec-draft-n-max = 2

I am also certain that these flags could be improved somehow depending on your system.