Nonprofit successfully deploys Qwen 3.6 with vLLM on dual RTX 3090 GPUs for improved multi-user AI performance

A nonprofit organization switched to vLLM framework to achieve better performance serving multiple users on their AI server setup

Infrastructure uses 2x RTX 3090 GPUs with Docker containerization, running the cyankiwi/Qwen3.6-35B-A3B-AWQ-4bit model

Configuration includes tensor parallelism across both GPUs, prefix caching, speculative decoding with 2 tokens, and support for up to 32 concurrent sequences

System optimized with 0.85 GPU memory utilization, 65,536 token context length, and Qwen3-specific reasoning and tool-calling capabilities