LocalLLaMA
Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.
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Rules:
Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.
Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.
Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.
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The CUDA version is what matters the most (assuming you are on NVidia). Later CUDA versions have optimizations that earlier don't, this may in turn dictate the actual driver version you can use.
I guess some models will simply deactivate some optimizations if you don't have an appropriate version, though I mostly am aware of them failing in that case :-/
If you compare a model running on CUDA 11 vs a model running on CUDA 12, people may point out that it could be unfair, though this is generally nitpicky.
If you are worried about your perfs not being optimal, look in the log for messages like " was deactivated because was not available"
I see. When I run the inference engine containerized, will the container be able to run its own version of CUDA or use the host's version?
I am not sure, I have tried to avoid this whole situation in the last few years :-) IIRC it can have its own CUDA version, but double check that.