0. What I studied and what I found I studied nano-vLLM as a small LLM serving engine : modeled its prefill and decode costs, traced the implementation, and compared the predictions with Qwen3-0.6B BF16 on one RTX 3090 (24 GB) . Its compact runtime makes the connection between scheduling, memory management, and GPU work possible to follow end to end. Four findings organize this article: Finding Evidence at a glance Cold prefill uses substantial compute capacity T...