How do AI labs manage large GPU compute commitments today?

I’m trying to understand how companies with significant GPU workloads manage their compute capacity. For those working in ML infrastructure / MLOps / AI labs: - How do you choose between hyperscalers, neoclouds and smaller GPU providers? - When you need a large amount of GPUs for months, how do you know you’re getting a competitive price? - Have you ever committed to more capacity than you actually needed? What happened to the unused capacity? Curious to hear how people actually deal with this today. submitted by /u/lebaart [link] [comments]