diff --git a/README.md b/README.md
index a473b840010..b86893e1ebd 100644
--- a/README.md
+++ b/README.md
@@ -96,7 +96,7 @@ If you're using `DistributedDataParallel`, make the following changes:
 +  dist.init_process_group("xla", init_method='xla://')
 +
 +  model.to(xm.xla_device())
-+  ddp_model = DDP(model)
++  ddp_model = DDP(model, gradient_as_bucket_view=True)
 
 -  model = model.to(rank)
 -  ddp_model = DDP(model, device_ids=[rank])
diff --git a/contrib/kaggle/distributed-pytorch-xla-basics-with-pjrt.ipynb b/contrib/kaggle/distributed-pytorch-xla-basics-with-pjrt.ipynb
index b96bb346643..dcc426bc080 100644
--- a/contrib/kaggle/distributed-pytorch-xla-basics-with-pjrt.ipynb
+++ b/contrib/kaggle/distributed-pytorch-xla-basics-with-pjrt.ipynb
@@ -464,7 +464,7 @@
     "    # Optional for TPU v4 and GPU\n",
     "    xm.broadcast_master_param(model)\n",
     "\n",
-    "    model = DDP(model)\n",
+    "    model = DDP(model, gradient_as_bucket_view=True)\n",
     "\n",
     "    loss_fn = nn.MSELoss()\n",
     "    optimizer = optim.SGD(model.parameters(), lr=.001)\n",
diff --git a/docs/source/learn/pjrt.md b/docs/source/learn/pjrt.md
index 97f358f29e5..6fc84bf9de3 100644
--- a/docs/source/learn/pjrt.md
+++ b/docs/source/learn/pjrt.md
@@ -82,7 +82,7 @@ def _mp_fn(index):
 
 +  # Optional for TPU v4 and GPU
 +  xm.broadcast_master_param(model)
-  model = DDP(model)
+  model = DDP(model, gradient_as_bucket_view=True)
 
   loss_fn = nn.MSELoss()
   optimizer = optim.SGD(model.parameters(), lr=.001)
diff --git a/docs/source/perf/ddp.md b/docs/source/perf/ddp.md
index f3d32650f4e..dabe61cdc13 100644
--- a/docs/source/perf/ddp.md
+++ b/docs/source/perf/ddp.md
@@ -43,7 +43,7 @@ device](../API_GUIDE.md#running-on-a-single-xla-device).
 4.  Wrap the model with DDP.
 
     ``` python
-    ddp_model = DDP(model)
+    ddp_model = DDP(model, gradient_as_bucket_view=True)
     ```
 
 5.  Finally launch your model with xla specific launcher.
@@ -107,7 +107,7 @@ def demo_basic(rank):
     # create model and move it to XLA device
     device = xm.xla_device()
     model = ToyModel().to(device)
-    ddp_model = DDP(model)
+    ddp_model = DDP(model, gradient_as_bucket_view=True)
 
     loss_fn = nn.MSELoss()
     optimizer = optim.SGD(ddp_model.parameters(), lr=0.001)
diff --git a/examples/data_parallel/train_resnet_ddp.py b/examples/data_parallel/train_resnet_ddp.py
index 327b3f8cbbc..5d295e12e34 100644
--- a/examples/data_parallel/train_resnet_ddp.py
+++ b/examples/data_parallel/train_resnet_ddp.py
@@ -18,7 +18,7 @@ def __init__(self):
     dist.init_process_group('xla', init_method='xla://')
     super().__init__()
     self.model = DDP(
-        self.model, broadcast_buffers=False)
+        self.model, broadcast_buffers=False, gradient_as_bucket_view=True)
     self.optimizer = optim.SGD(self.model.parameters(), weight_decay=1e-4)
 
 
diff --git a/test/test_train_mp_imagenet.py b/test/test_train_mp_imagenet.py
index cc761c875e7..bec580c3831 100644
--- a/test/test_train_mp_imagenet.py
+++ b/test/test_train_mp_imagenet.py
@@ -258,7 +258,9 @@ def train_imagenet():
   xm.broadcast_master_param(model)
 
   if FLAGS.ddp:
-    model = DDP(model, broadcast_buffers=False)
+    # gradient_as_bucket_view=True saves memory and can be used so long as
+    # we are not calling `detach_()` on the gradients.
+    model = DDP(model, broadcast_buffers=False, gradient_as_bucket_view=True)
 
   writer = None
   if xm.is_master_ordinal():
diff --git a/test/test_train_mp_mnist.py b/test/test_train_mp_mnist.py
index 315f4b200f6..9e470719f27 100644
--- a/test/test_train_mp_mnist.py
+++ b/test/test_train_mp_mnist.py
@@ -138,7 +138,7 @@ def train_mnist(flags, **kwargs):
   xm.broadcast_master_param(model)
 
   if flags.ddp:
-    model = DDP(model)
+    model = DDP(model, gradient_as_bucket_view=True)
   writer = None
   if xm.is_master_ordinal():
     writer = test_utils.get_summary_writer(flags.logdir)