  Pretrained general-purpose language models can achieve state-of-the-art
accuracies in various natural language processing domains by adapting to
downstream tasks via zero-shot, few-shot and fine-tuning techniques. Because of
their success, the size of these models has increased rapidly, requiring
high-performance hardware, software, and algorithmic techniques to enable
training such large models. As the result of a joint effort between Microsoft
and NVIDIA, we present details on the training of the largest monolithic
transformer based language model, Megatron-Turing NLG 530B (MT-NLG), with 530
billion parameters. In this paper, we first focus on the infrastructure as well
as the 3D parallelism methodology used to train this model using DeepSpeed and
Megatron. Next, we detail the training process, the design of our training
corpus, and our data curation techniques, which we believe is a key ingredient
to the success of the model. Finally, we discuss various evaluation results, as
well as other interesting observations and new properties exhibited by MT-NLG.
We demonstrate that MT-NLG achieves superior zero-, one-, and few-shot learning
accuracies on several NLP benchmarks and establishes new state-of-the-art
results. We believe that our contributions will help further the development of
large-scale training infrastructures, large-scale language models, and natural
language generations.
