feeding data to modern gpu clusters is going to give me an ulcer
just a quick vent because the data science team is driving me insane this week.
they got a massive budget approved for some new ML initiative, bypassed infra completely, and just provisioned a bunch of heavy server mania bare metal nodes to crunch their datasets. fine, cool, whatever.
but now i have a p1 ticket sitting in my queue because their training pipelines are "stalling" and they are instantly blaming the storage backend.
brother, you are trying to feed tens of thousands of random small file reads per second into multiple high-end GPUs over a legacy 10G iSCSI hybrid array that we bought in 2019. the controllers are quite literally screaming for their lives.
it is so frustrating how the entire tech industry is obsessing over raw compute right now, but absolutely nobody thinks about the storage throughput required to actually keep those pipelines fed until the IO wait times hit the roof.
tell me im not the only one dealing with these new AI workloads completely nuking their legacy SANs. im so tired.