No GPU left behind: Unlocking Efficiency with Co-located vLLM in TRL

This isn't new; it's just smart deployment plumbing. The real win here is minimizing wasted VRAM when you're running inference at scale. Co-locating vLLM with TRL containers just means we stop fighting the scheduler and GPU fragmentation. It’s about maximizing throughput on existing hardware, not magically getting more compute.

If you're running multiple models, you're wasting cycles waiting for context switching. By tightly binding the serving engine, you cut down latency and overhead. It’s a necessary operational tweak, not a core AI innovation.