Inclusion criteria

To identify ML hardware, we annotated chips used for ML training in our database of Notable AI Models. We additionally added ML hardware that has not been documented in training those systems, but is clearly manufactured for ML - based on its description, supported numerical formats, or belonging to the same chip family as other ML hardware.

We use hardware datasheets, documented for each chip in the dataset, to fill in key information such as computing performance, die size, etc. Not all information is available, or even applicable, for all hardware, so columns can be left empty. We additionally use other sources, such as news coverage or hardware price archives, to fill in the price on release.