diff --git a/docs/_tutorials/zero.md b/docs/_tutorials/zero.md
index 7e5aa0d3672a..9b39519490d2 100644
--- a/docs/_tutorials/zero.md
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@@ -31,7 +31,10 @@ We demonstrate the benefits of ZeRO stage 1 by showing that it enables data para
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Training this model without ZeRO fails with an out-of-memory (OOM) error as shown below:
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A key reason why this model does not fit in GPU memory is that the Adam optimizer states for the model consume 18GB; a significant portion of the 32GB RAM. By using ZeRO stage 1 to partition the optimizer state among eight data parallel ranks, the per-device memory consumption can be reduced to 2.25GB, thus making the model trainable. To enable ZeRO stage 1, we simply update the DeepSpeed json config file as below:
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As seen above, we set two fields in the **zero_optimization** key. Specifically we set the _stage_ field to 1, and the optional _reduce_bucket_size_ for gradient reduction to 500M. With ZeRO stage 1 enabled, the model can now train smoothly on 8 GPUs without running out of memory. Below we provide some screenshots of the model training:
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From the nvidia-smi screenshot above we can see that only GPUs 6-7 are being used for training the model. With ZeRO stage 1 we can further reduce the per-device memory consumption by increasing the data parallelism degree. These memory savings can be leveraged to either increase model size and/or batch size. In contrast, such benefits are not possible with data parallelism alone.
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Here is a screenshot of the training log:
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Here is a screenshot of nvidia-smi showing GPU activity during training:
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Congratulations! You have completed the ZeRO tutorial.