If so, that's fine-ish, but then people have to wait (you're either full and people are waiting or you're at less than 100%). My preference is to run for XX minutes per job on-demand (per person). If you have tons of non-overlapping users, you can absolutely aggregate them. But how many do you buy and how quickly do you upgrade to newer hardware?
[1] https://cloud.google.com/dataproc/docs/concepts/gpu-clusters
That being said - it can usually be every 6 months with a renewal of every 2 years or so. Your hardware timelines aren't far off. That being said - I was getting that upgrade cycles don't matter as much.
GPU clusters are enough of a new thing for enterprise yet that the jobs being run aren't even that high spec yet.
My opinion nothing more not claiming this is fact: Google has marched so far ahead of the rest of the world they aren't really paying attention to where current clusters and usage are. It seems a lot of the cloud usage is oriented towards startups and researchers (which isn't a bad thing, most folks in DL are researchers).
For enterprise, they might offload some workloads. There are definitely some workloads where cloud resources (spin up and shut down) make a ton of sense. Cloud servers are overly expensive otherwise.
It's cheaper, allows gradual adoption of deep learning and is a familiar toolset for folks already doing some sort of machine learning.
I won't comment on research (not our domain). Depreciation on hardware is pretty standard - that being said it also comes with established SKUs from dell,hp,cisco,.. with proper support.
Analytics clusters (while hard to manage) are fairly robust already to job failures. The cost savings just makes a ton more sense when you are doing continuous workloads for different use cases.