vLLM On-Demand Gateway: Zero-VRAM Standby for Local LLMs on Consumer GPUs

The Problem: vLLM Hogs Your GPU 24/7 If you run a local LLM with vLLM, you know the pain. The moment you start the server, it claims ~90% of your VRAM and never lets go — even when nobody's asking it anything. On a dedicated inference server, that's fine. But on a single consumer GPU (RTX 5090 in my case), I also need VRAM for: Shogi engine (DL-based, needs ~4GB VRAM) Whisper transcription (large-v3, GPU-accelerated) Training runs, experiments, occasional gaming Running vLLM permanently means everything else fights for scraps. Killing and restarting vLLM manually every time is not a workflow — it's a chore. The Solution: A Gateway That Manages vLLM's Lifecycle I wrote a single-file FastAPI gateway (vllm_gateway.py, ~390 lines) that: Listens on port 8000 with near-zero VRAM usage Auto-starts vLLM on an internal port (8100) when a request arrives Auto-stops vLLM after 10 minutes of idle, fully freeing VRAM Rewrites tool calls from Nemotron's <TOOLCALL> format to OpenAI-compatible t