I’m currently working on the design of a framework for GPU fleet management. We’re living in a crowded data center reality where everybody wants “hero” compute — dense GPUs, fast networking, and delivery that’s closer to the edge. We’re in a land-grab phase where every business wants to be everywhere, but most teams are discovering the same thing: buying GPUs is the easy part. Operating them as a coherent fleet is the hard part. ...

The “Everything Is Green” Problem Here’s a realistic scenario I’ve seen in different forms across fleets (this is a composite, not a single true story with exact numbers): A training run is supposed to take ~3–4 weeks. Two weeks in, someone notices the timeline slipping. Not a crash. Not a failure. Just… slow. The job is running 10–30% behind plan, and nobody can point to a smoking gun. The dashboards look perfect: ...