Optimizing Qwen 3.6 27B/35B on RTX 3090: Flags, Quantization, and Auto-Routing
A developer running Qwen 3.6 models locally on an RTX 3090 (24GB VRAM), Ryzen 5700X, 64GB RAM, Windows 11, is hitting performance and reliability issues. They're using llama-server with custom flags and seeking advice on quant choice, throughput, and automatic model routing.
Commands and Quantizations
35B (UD Q4_K_M):
llama-server.exe -m "path\Qwen3.6-35B-A3B-UD-Q4_K_M.gguf" -ngl 99 -c 131072 -np 2 -fa on -ctk f16 -ctv f16 -b 2048 -ub 512 -t 8 --mlock -rea on --reasoning-budget 2048 --reasoning-format deepseek --jinja --metrics --slots --port 8081 --host 0.0.0.027B (UD Q4_K_XL):
llama-server.exe -m "path\Qwen3.6-27B-UD-Q4_K_XL.gguf" -ngl 99 -c 196608 -np 1 -fa on -ctk q8_0 -ctv q8_0 -b 2048 -ub 512 -t 8 --no-mmap -rea on --reasoning-budget -1 --reasoning-format deepseek --jinja --metrics --slots --port 8081 --host 0.0.0.0Reported Issues
- 35B too slow – even simple iterative tasks feel unusable.
- 27B faster but unreliable – code output breaks; simple tasks can take 20–30 minutes.
- Manual model switching – must kill server, paste new command, reload model.
Specific Questions
- Are the flags suboptimal? (e.g., context size, batch size, cache type)
- Which quant / model gives best balance of speed and coding accuracy on 24GB VRAM?
- How to auto-switch models per request, or keep multiple models warm and route?
Context
The user runs Hermes agent on a Raspberry Pi 5 for scraping and automation, and local coding with OpenCode/QwenCode. They want a setup that doesn't require manual server restarts.
📖 Read the full source: r/LocalLLaMA
👀 See Also
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Running Qwen3.6-35B-A3B with ~190k Context on 8GB VRAM + 32GB RAM – Setup & Benchmarks
A Reddit user shares a working llama.cpp configuration for Qwen3.6-35B-A3B GGUF models on an RTX 4060 (8GB VRAM) + 32GB DDR5, achieving 37-51 tok/s at 192k context using TurboQuant and specific flags.
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