diff --git a/docs/source/guides/speed.rst b/docs/source/guides/speed.rst
index 0f5a34e699..0b8edb43b7 100644
--- a/docs/source/guides/speed.rst
+++ b/docs/source/guides/speed.rst
@@ -145,6 +145,16 @@ some references, [`1 <https://discuss.pytorch.org/t/guidelines-for-assigning-num
 
 The best thing to do is to increase the ``num_workers`` slowly and stop once you see no more improvement in your training speed.
 
+For debugging purposes or for dataloaders that load very small datasets, it is desirable to set ``num_workers=0``. However, this will always log a warning for every dataloader with ``num_workers <= min(2, os.cpu_count())``. In such cases, you can specifically filter this warning by using:
+
+.. code-block:: python
+
+    import warnings
+
+    warnings.filterwarnings(
+        "ignore", ".*does not have many workers. Consider increasing the value of the `num_workers` argument*"
+    )
+
 Spawn
 """""
 When using ``strategy=ddp_spawn`` or training on TPUs, the way multiple GPUs/TPU cores are used is by calling ``.spawn()`` under the hood.