Running HauhauCS/Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive Q4_K_M with llama.cpp at ~600 tok/s prefill and 23 tok/s decode, 131k context window, Q8 KV cache - on an RTX 2060 6GB + 32GB DDR4 RAM. Speeds start at ~600 tok/s prefill / 23 tok/s decode on an empty KV cache. As context grows they settle down - around 90k context it stabilizes at roughly 485 tok/s prefill and 15 tok/s decode, and holds there. The vision projector runs on CPU (--no-mmproj-offload), which keeps VRAM usage under ~5.2 GB and avoids OOM / GPU crashes. Image encoding is slower on CPU, but it buys ~1GB of VRAM. Most MoE expert layers also run on CPU (--n-cpu-moe 39), which is how a 35B model fits in 6GB VRAM in the first place. Launch command: bat @echo off cd /d "%~dp0" "%~dp0llama-server.exe" ^ -m "C:\Qwen3.6-35B-A3B-Uncensored-Q4_K_M\Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-Q4_K_M.gguf" ^ --mmproj "C:\Qwen3.6-35B-A3B-Uncensored-Q4_K_M\mmproj-Qwen3.6-35B-A3B-Uncensored-HauhauCS-Aggressive-f16.gguf" ^ --no-mmproj-offload ^ -ngl 99 ^ --n-cpu-moe 39 ^ -c 131072 ^ -np 1 ^ -t 6 ^ -tb 10 ^ -b 2048 ^ -ub 2048 ^ -fa on ^ -ctk q8_0 ^ -ctv q8_0 ^ --load-mode mmap+mlock ^ --jinja ^ --reasoning-format deepseek ^ --reasoning-preserve ^ --spec-type none ^ --image-min-tokens 1024 ^ --temp 0.6 ^ --top-p 0.95 ^ --top-k 20 ^ --min-p 0 ^ --alias Qwen3.6-35B-A3B-Uncensored-Q4_K_M ^ --host 127.0.0.1 ^ --port 8081 pause Hardware: RTX 2060 6GB + 32GB DDR4 RAM + i5-10400F CPU Context: 131072 tokens, Q8_0 KV cache Hope this helps someone. If anyone has tips to make the launch command even better, drop them in the comments - I'm out of ideas :D   submitted by   /u/Szadbaverem69 [link]   [comments]