diff --git a/monai/data/thread_buffer.py b/monai/data/thread_buffer.py
index cdd7c05f31..e21af69813 100644
--- a/monai/data/thread_buffer.py
+++ b/monai/data/thread_buffer.py
@@ -89,6 +89,12 @@ class ThreadDataLoader(DataLoader):
     on the same batch will still produce good training with minimal short-term overfitting while allowing a slow batch
     generation process more time to produce a result.
 
+    Another typical usage is to accelerate light-weight preprocessing (usually cached all the deterministic transforms
+    and no IO operations), because it leverages the separate thread to execute preprocessing to avoid unnecessary IPC
+    between multiple workers of DataLoader. And as CUDA may not work well with the multi-processing of DataLoader,
+    `ThreadDataLoader` can be useful for GPU transforms. For more details:
+    https://github.com/Project-MONAI/tutorials/blob/master/acceleration/fast_model_training_guide.md.
+
     See:
         * Fischetti et al. "Faster SGD training by minibatch persistency." ArXiv (2018) https://arxiv.org/abs/1806.07353
         * Dami et al., "Faster Neural Network Training with Data Echoing" ArXiv (2020) https://arxiv.org/abs/1907.05550
@@ -99,20 +105,15 @@ class ThreadDataLoader(DataLoader):
         dataset: input dataset.
         buffer_size: number of items to buffer from the data source.
         buffer_timeout: time to wait for an item from the buffer, or to wait while the buffer is full when adding items.
-        num_workers: number of the multi-processing workers in PyTorch DataLoader.
-        repeats: number of times to yield the same batch
+        repeats: number of times to yield the same batch.
+        kwargs: other arguments for `DataLoader` except for `dataset`.
+
     """
 
     def __init__(
-        self,
-        dataset: Dataset,
-        buffer_size: int = 1,
-        buffer_timeout: float = 0.01,
-        num_workers: int = 0,
-        repeats: int = 1,
-        **kwargs,
+        self, dataset: Dataset, buffer_size: int = 1, buffer_timeout: float = 0.01, repeats: int = 1, **kwargs
     ):
-        super().__init__(dataset, num_workers, **kwargs)
+        super().__init__(dataset, **kwargs)
         self.buffer_size = buffer_size
         self.buffer_timeout = buffer_timeout
         self.repeats = repeats