For inference, even with continuous batching, getting 100% MFUs is basically impossible to do in practice. Even the frontier labs struggle with this in highly efficient infiniband clusters. Its slightly better with training workloads just due to all the batching and parallel compute, but still mostly unattainable with consumer rigs (you spend a lot of time waiting for I/O).
I also don't think the 100% util is necessary either, to be fair. I get a lot of value out of my two rigs (2x rtx pro 6000, and 4x 3090) even though it may not be 24/7 100% MFU. I'm always training, generating datasets, running agents, etc. I would never consider this a positive ROI measured against capex though, that's not really the point.
No I'm saying there are quite a few more bottlenecks than that (I/O being a big one). Even in the more efficient training frameworks, there's per-op dispatch overhead in python itself. All the boxing/unboxing of python objects to C++ handles, dispatcher lookup + setup, all the autograd bookkeeping, etc.
All of the bottlenecks in sum is why you'd never get to 100% MFUs (but I was conceding you probably don't need to in order to get value)
I also don't think the 100% util is necessary either, to be fair. I get a lot of value out of my two rigs (2x rtx pro 6000, and 4x 3090) even though it may not be 24/7 100% MFU. I'm always training, generating datasets, running agents, etc. I would never consider this a positive ROI measured against capex though, that's not really the point.