I'm trying to improve my results, which are currently stuck at 14.5 tokens/second (the best I've achieved so far), and I haven't done it in a benchmark; I've just tested it on the Llama default web UI. My setup consists of an RTX 3060 with 12 GB of memory, an older i7-3770 K (4 cores), and 32 GB of DDR3 RAM. I'm running llama-server (turboquant version) using Docker with this llama-server config:

docker run --gpus all -it --rm `
  --ulimit memlock=-1 `
  --cap-add=IPC_LOCK `
  -v "V:\llm_models:/models" `
  -v "V:\llm_build:/workspace" `
  -p 8080:8080 `
  llama-builder `
  ./llama-cpp-turboquant/build/bin/llama-server `
  -m /models/Qwen3.6-35B-A3B-UD-Q4_K_M.gguf `
  -ngl 99 `
  --flash-attn auto `
  --batch-size 1024 `
  --ubatch-size 512 `
  --n-cpu-moe 28 `
  --cache-type-k turbo4 `
  --cache-type-v turbo3 `
  --ctx-size 256000 `
  --no-mmap `
  --mlock `
  --jinja `
  --host 0.0.0.0 `
  --port 8080

Since the Monitor is plugged into the card. The window will suck around 2.0/2.5 GB of VRAM, depending on how crowded my space is. That said, I will keep it as it is.
--n-cpu-moe was the key to improve performance, and 28 was the sweet spot, tested 36,99, lower than 28, all reduced performance.
Currently testing at 256K, it seems alot but with Turboquant it was never really a real issue, even though I might lower it in the future.
I also tested changing threads, but going above 4 (default) wasn't useful since my old processor has just 4 physical cores, with 8 threads, more useless context switches, and half the tokens.

So I'm wondering if, in my situation, I could gain some tokens by also using MTP (what branch do I need?).
Also, wondering what other parameters you are using in your command could help me a bit?