Training a Recommender System on DGX A100 with 100B+ Parameters in TensorFlow 2 – NVIDIA Technical Blog News and tutorials for developers, data scientists, and IT admins 2025-07-03T22:20:47Z http://www.open-lab.net/blog/feed/ Tomasz Grel <![CDATA[Training a Recommender System on DGX A100 with 100B+ Parameters in TensorFlow 2]]> http://www.open-lab.net/blog/?p=46168 2023-02-13T18:51:50Z 2022-04-05T22:36:21Z Deep learning recommender systems often use large embedding tables. It can be difficult to fit them in GPU memory. This post shows you how to use a combination...]]> Deep learning recommender systems often use large embedding tables. It can be difficult to fit them in GPU memory. This post shows you how to use a combination...

Deep learning recommender systems often use large embedding tables. It can be difficult to fit them in GPU memory. This post shows you how to use a combination of model parallel and data parallel training paradigms to solve this memory issue to train large deep learning recommender systems more quickly. I share the steps that my team took to efficiently train a 113 billion-parameter��

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