Large transformer models require optimizing inference by batching a huge number of queries to amortize the expensive price of loading weights from memory and make inference much cheaper. This centralization of inference is expected to be similar to the centralization of training, due to the need for a large number of grouped users. The power-hungry nature of training is attributed to the need to compute the contribution of each weight to the final error by running the model backwards, which requires about double the compute and different network primitives compared to running forwards.

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