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Probably more than 100x for inference. Not only are you drastically reducing the number of bits and replacing float math with integer math, you can do matrix multiplication with only addition (as pointed out in the BitNet b1.58 paper). Additions require a lot less hardware to implement than multiplication. Adding one-bit or two-bit numbers requires barely any hardware at all. A traditional two-bit adder without carry bit is three xor gates and an and gate.

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to me the most exciting thing is that if is training that is speed up on the order of 100x-1000x, a large cluster may be well suited to gradient-descend hyperparameter tuning parameters by LLM training again and again at scale -- this is the first foot in a door towards an AI that iteratively may improve itself

LoRA training should benefit from the same speed-up, because the 1-bit weights will be frozen and all you need for both the forward and backward pass is a binary matmul, then maybe cast after to get more stable gradients.

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