this post was submitted on 04 Dec 2023
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Machine Learning
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It is very normal/usual/expected that a GPU won't run at 100% unless you provide it with enough parallel computations to perform (either extremely large layers or a big batch of samples). There's just so much overhead in so many places, it's impossible to be efficient at a small scale, and there's no one thing we can point to and say "that's why it's slow". My best advice is to look into the optimization possibilities provided by the model/framework/version you're using. I'm in pytorch, and use things like torch.jit.script, torch.jit.trace, torch.compile, torch.autocast, etc.