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 0.0.0.0

27B (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 0.0.0.0

Reported 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.