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Machine Learning Hardware Documentation
Epoch's AI Machine Learning Hardware database is a collection of AI accelerators, such as graphics processing units (GPUs) and tensor processing units (TPUs), used to develop and deploy machine learning models in the deep learning era.
Overview
Epoch AI’s Machine Learning Hardware dataset is a collection of AI accelerators, such as graphics processing units (GPUs) and tensor processing units (TPUs), used to develop and deploy machine learning models in the deep learning era.
This documentation describes the processors included in the dataset, its records, data fields, and definitions, and a changelog and acknowledgements.
The data is available on our website as a visualization or table, and is available for download as a CSV file, updated daily. For a quick-start example of loading the data and working with it in your research, see this Google Colab demo notebook.
If you would like to ask any questions about the data, or suggest hardware that should be added, feel free to contact us at data@epochai.org.
If this data is useful for you, please cite it as illustrated:
Citation
Epoch AI, ‘Data on Machine Learning Hardware’. Published online at epoch.ai. Retrieved from: ‘https://epoch.ai/data/machine-learning-hardware’ [online resource]BibTeX citation
@misc{EpochMLHardware2024,
title = "Data on Machine Learning Hardware",
author = {{Epoch AI}},
year = 2024,
url = {https://epoch.ai/data/machine-learning-hardware},
note = "Accessed: "
}Inclusion
This dataset focuses on machine learning processors. These are processors used to train and deploy ML and AI models, especially those included in our Notable AI Models dataset. Here we explain the inclusion and search process and give an overview of data sources.
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.
Records
This dataset has fields containing various processor details, attributes, and specifications. Records in the dataset have information about three broad areas:
Specifications about the processors, such as their clock speed, memory capacity, and performance.
Provenance details, such as the manufacturer and release date.
Metadata, such as sources containing information about the hardware, and a list of models it has been used to produce.
We provide a comprehensive guide to the data fields, below. This includes examples taken from the NVIDIA A100 SXM4 40 GB datacenter GPU, which is one of the most popular hardware used for machine learning. If you would like to request a field be added, contact us at data@epochai.org.
Acknowledgements
The data have been collected by Epoch AI’s employees and collaborators, including Marius Hobbhahn, Lennart Heim, Gökçe Aydos, Robi Rahman, Josh You, Bartosz Podkanowicz, Luke Frymire, Natalia Martemianova, and James Sanders.
This documentation was written by Robi Rahman and David Owen.