diff --git a/CHANGELOG.md b/CHANGELOG.md
index e8b948b26d..266d789765 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -358,6 +358,9 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Fixed freeing data iterators in loop `on_run_end` ([#9386](https://github.com/PyTorchLightning/pytorch-lightning/pull/9386))
 
 
+- Fixed collision of user argument when using ShardedDDP ([#9512](https://github.com/PyTorchLightning/pytorch-lightning/pull/9512))
+
+
 ## [1.4.5] - 2021-08-31
 
 - Fixed reduction using `self.log(sync_dict=True, reduce_fx={mean,max})` ([#9142](https://github.com/PyTorchLightning/pytorch-lightning/pull/9142))
diff --git a/pytorch_lightning/plugins/training_type/sharded.py b/pytorch_lightning/plugins/training_type/sharded.py
index 817dfd65ce..8ff960531e 100644
--- a/pytorch_lightning/plugins/training_type/sharded.py
+++ b/pytorch_lightning/plugins/training_type/sharded.py
@@ -36,11 +36,14 @@ class DDPShardedPlugin(DDPPlugin):
 
     def configure_ddp(self) -> None:
         self._wrap_optimizers()
+
+        if "reduce_buffer_size" not in self._ddp_kwargs:
+            # For multi-node training, enabling bucketing will improve performance.
+            self._ddp_kwargs["reduce_buffer_size"] = self._REDUCE_BUFFER_SIZE_DEFAULT if self.num_nodes > 1 else 0
+
         self._model = ShardedDataParallel(
             LightningShardedDataParallel(self.model),
             sharded_optimizer=self.lightning_module.trainer.optimizers,
-            # For multi-node training, enabling bucketing will improve performance.
-            reduce_buffer_size=self._REDUCE_BUFFER_SIZE_DEFAULT if self.num_nodes > 1 else 0,
             **self._ddp_kwargs
         )
         setattr(self._model, "require_backward_grad_sync", False)
diff --git a/tests/plugins/test_sharded_plugin.py b/tests/plugins/test_sharded_plugin.py
index f3ff6d8e5c..adee6e3ba2 100644
--- a/tests/plugins/test_sharded_plugin.py
+++ b/tests/plugins/test_sharded_plugin.py
@@ -285,3 +285,27 @@ def test_custom_kwargs_sharded(tmpdir, cls):
     args, kwargs = mock_sharded.call_args
     assert "reduce_fp16" in kwargs
     assert kwargs["reduce_fp16"]
+
+
+@RunIf(skip_windows=True, fairscale=True)
+@mock.patch("pytorch_lightning.plugins.DDPShardedPlugin._wrap_optimizers", autospec=True)
+@pytest.mark.parametrize(["params", "expected_buffer_size"], [(dict(), 0), (dict(reduce_buffer_size=128), 128)])
+@pytest.mark.parametrize("num_nodes", [1, 2])
+def test_custom_kwargs_sharded_reduce_buffer_size(tmpdir, params, expected_buffer_size, num_nodes):
+    """Tests to ensure that ``reduce_buffer_size`` is correctly set based on user kwargs."""
+    plugin = DDPShardedPlugin(**params)
+    plugin.num_nodes = num_nodes
+
+    with mock.patch.object(plugin, "_model", autospec=True):
+        with mock.patch(
+            "pytorch_lightning.plugins.training_type.sharded.ShardedDataParallel", autospec=True
+        ) as mock_sharded:
+            plugin.configure_ddp()
+    args, kwargs = mock_sharded.call_args
+    assert "reduce_buffer_size" in kwargs
+
+    if num_nodes > 1 and len(params) == 0:
+        # If user has not specified a buffer size and we're using multiple nodes, check to see if default is set
+        assert kwargs["reduce_buffer_size"] == DDPShardedPlugin._REDUCE_BUFFER_SIZE_DEFAULT
+    else:
+        assert kwargs["reduce_buffer_size"] == expected_buffer_size