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I think this is a very rough estimation , the actual value should depend on batch size, token length and the embedding size(or hidden layer dimension).
For example a 13B model, has 40 layers and the token length is 4096 , embedding size is 8192, if using batchsize 1, it needs 1 (batchsize) * 8192 (embedding size) * 2 (byets, FP16) * 4096 (token length) * 40 (layer) ~= 2560M, about 0.625 M per token
I am finding the 1MB GPU ram usage per token while inferencing calculation a bit hard to understand --- also not what I am seeing in practice.
Any insights on how this number was computed ?
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