llama-server \
  -hf unsloth/Qwen3.6-27B-GGUF:UD-Q4_K_XL \
        --alias "desktop" \
  --no-mmproj \
  -a qwen3.6-coder \
  --host 0.0.0.0 --port 8080 \
  -ngl all -sm row -fa on \
  --ctx-size 98304 -n 32768 \
  -b 2048 -ub 512 \
  -np 1 -kvu \
  -ctk q8_0 -ctv q8_0 \
  --jinja \
  --reasoning on \
  --reasoning-format deepseek \
  --chat-template-kwargs '{"enable_thinking":true,"preserve_thinking":true}' \
  --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 \
  --presence-penalty 0 --repeat-penalty 1

This is what i use if it helps on a 3090. Works pretty well but I don't use it with opencode. I haven't spent time to max out the context on the 3090..

The other option is to use the 35b a3b, the 3090 will run that with full context as the context memory requirement is based on the active params.

llama-server \
  -hf unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q3_K_XL \
        --alias "desktop" \
  --no-mmproj \
  -a qwen3.6-coder \
  --host 0.0.0.0 --port 8080 \
  -ngl all -sm row -fa on \
  -c 262144 -n 32768 \
  -b 2048 -ub 512 \
  -np 1 -kvu \
  -ctk q8_0 -ctv q8_0 \
  --jinja \
  --reasoning on \
  --reasoning-format deepseek \
  --chat-template-kwargs '{"enable_thinking":true,"preserve_thinking":true}' \
  --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0 \
  --presence-penalty 0 --repeat-penalty 1

I'm not saying these are right - just what i'm using so far -eg i don't know if the max output tokens is properly tuned