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MTBF is absolutely not six years if you’re running your H100 nodes at peak load and heat soaking the shit out of them. ML workloads are particularly hard on GPU RAM in particular, and sustained heat load on that particular component type on the board is known to degrade performance and integrity.
As to Meta’s (or MS, or OpenAI, or what have you) doc on MTBF: I don’t really trust them on that, because they’re a big player in the “AI” bubble, so of course they’d want to give the impression that the hardware they’re using in their data centers still have a bunch of useful life left. That’s a direct impact to their balance sheet. If they can misrepresent extremely expensive components that they have a shitload of as still being worth a lot, instead of being essentially being salvage/parts only, I would absolutely expect them to do that. Especially in the regulatory environment in which we now exist.
I mean, we really don't have the data to prove this either way.
https://www.tomshardware.com/tech-industry/artificial-intelligence/faulty-nvidia-h100-gpus-and-hbm3-memory-caused-half-of-the-failures-during-llama-3-training-one-failure-every-three-hours-for-metas-16384-gpu-training-cluster
Meta's training of Llama3 405B model had a 1.34% failure rate for GPUs over the 54 days it ran, across 16387 gpus. It's not likely that all of those faults led to bricked hardware either, they could have just lost part of their performance or memory.
The real question is does that test scale to the long term, often with hardware like this there's a bathtub curve for failure. If those units used were brand new, many of the failures could have just been the initial wave of failures, and there could be a long period of relative stability that hadn't even been seen yet.
GPU based coin mining demonstrated that GPUs often had a lifespan over 5 years of constant use before failure on consumer cards in often less than ideal operating conditions.