Best config for Qwen3.6 27b / llama.cpp / opencode

Reddit r/LocalLLaMA Tools
llama-cpp qwen3-6 gguf inference config

Summary

Community thread sharing optimized llama.cpp launch commands for running the 27B Qwen3.6 GGUF model with long 100K-512K context on multi-GPU setups.

Please share your best config <3 Windows 2x3080 20GB VRAM, DDR4 256GB RAM , llama.ccp, On 100K filled context i have 400/11 pp/tg (My setup): "A:/0_llama_server/llama-server.exe" -m "a:\0_LM_Studio\unsloth\Qwen3.6-27B-GGUF\Qwen3.6-27B-UD-Q5_K_XL.gguf" --port 8080 --alias qwen3.5:27b -ngl 999 --threads 22 --flash-attn on --host[0.0.0.0](http://0.0.0.0)--no-mmap -mg 1 --batch-size 1024 --ubatch-size 512 --ctx-checkpoints 128 --ctx-size 196610 --reasoning on --jinja --draft-max 128 --spec-ngram-size-n 48 --draft-min 2 --spec-type ngram-mod --temp 0.6 --top-p 0.95 --top-k 20 --min-p 0.00 --repeat_penalty 1.0 --presence_penalty 0.0 --chat-template-kwargs "{"preserve_thinking":true}" --tensor-split 0.46,0.54 DGX (user Impossible_Art9151): llama-server -hf unsloth/Qwen3.6-27B-GGUF:UD-Q8_K_XL --host 0.0.0.0 --port 8095 --ctx-size 512000 --no-mmap --parallel 2 --flash-attn on --n-gpu-layers 999 -chat-template-kwargs "{"preserve_thinking":true}" --temp 0.7 --top-p 0.95 --top-k 20 --min-p 0.00 --repeat_penalty 1.0 --presence_penalty 0.0
Original Article

Similar Articles

Best Settings for 48GB VRAM + Qwen 3.6 27B

Reddit r/LocalLLaMA

A user shares optimized settings for running Qwen3.6 27B (Q8_0) on a dual GPU setup (RTX 4090 + RTX 3090) with llama.cpp, achieving 75-100 t/s and 1500 pp with 250k context.

Qwen 3.6 27B flags/settings in llama.cpp

Reddit r/LocalLLaMA

A user shares their llama.cpp server configuration for running Qwen 3.6 27B on an RTX 5090, achieving 80-100 t/s, and asks the community for alternative settings and tips.

Running Qwen3.6 35b a3b on 8gb vram and 32gb ram ~190k context

Reddit r/LocalLLaMA

The author shares a high-performance local inference configuration for running Qwen3.6 35B A3B on limited hardware (8GB VRAM, 32GB RAM) using a modified llama.cpp with TurboQuant support, achieving ~37-51 tok/sec with ~190k context.