diff --git a/examples/multigpu/graphbolt/node_classification.py b/examples/multigpu/graphbolt/node_classification.py
index 2d3344ce301c..5ef93311fe55 100644
--- a/examples/multigpu/graphbolt/node_classification.py
+++ b/examples/multigpu/graphbolt/node_classification.py
@@ -126,9 +126,6 @@ def create_dataloader(
         shuffle=shuffle,
         drop_uneven_inputs=drop_uneven_inputs,
     )
-    datapipe = datapipe.sample_neighbor(graph, args.fanout)
-    datapipe = datapipe.fetch_feature(features, node_feature_keys=["feat"])
-
     ############################################################################
     # [Note]:
     # datapipe.copy_to() / gb.CopyTo()
@@ -137,8 +134,14 @@ def create_dataloader(
     # [Output]:
     # A CopyTo object copying data in the datapipe to a specified device.\
     ############################################################################
-    datapipe = datapipe.copy_to(device)
-    dataloader = gb.DataLoader(datapipe, num_workers=args.num_workers)
+    if not args.cpu_sampling:
+        datapipe = datapipe.copy_to(device, extra_attrs=["seed_nodes"])
+    datapipe = datapipe.sample_neighbor(graph, args.fanout)
+    datapipe = datapipe.fetch_feature(features, node_feature_keys=["feat"])
+    if args.cpu_sampling:
+        datapipe = datapipe.copy_to(device)
+
+    dataloader = gb.DataLoader(datapipe, args.num_workers)
 
     # Return the fully-initialized DataLoader object.
     return dataloader
@@ -272,15 +275,18 @@ def run(rank, world_size, args, devices, dataset):
         rank=rank,
     )
 
-    graph = dataset.graph
-    features = dataset.feature
+    # Pin the graph and features to enable GPU access.
+    if not args.cpu_sampling:
+        dataset.graph.pin_memory_()
+        dataset.feature.pin_memory_()
+
     train_set = dataset.tasks[0].train_set
     valid_set = dataset.tasks[0].validation_set
     test_set = dataset.tasks[0].test_set
     args.fanout = list(map(int, args.fanout.split(",")))
     num_classes = dataset.tasks[0].metadata["num_classes"]
 
-    in_size = features.size("node", None, "feat")[0]
+    in_size = dataset.feature.size("node", None, "feat")[0]
     hidden_size = 256
     out_size = num_classes
 
@@ -291,8 +297,8 @@ def run(rank, world_size, args, devices, dataset):
     # Create data loaders.
     train_dataloader = create_dataloader(
         args,
-        graph,
-        features,
+        dataset.graph,
+        dataset.feature,
         train_set,
         device,
         drop_last=False,
@@ -301,8 +307,8 @@ def run(rank, world_size, args, devices, dataset):
     )
     valid_dataloader = create_dataloader(
         args,
-        graph,
-        features,
+        dataset.graph,
+        dataset.feature,
         valid_set,
         device,
         drop_last=False,
@@ -311,8 +317,8 @@ def run(rank, world_size, args, devices, dataset):
     )
     test_dataloader = create_dataloader(
         args,
-        graph,
-        features,
+        dataset.graph,
+        dataset.feature,
         test_set,
         device,
         drop_last=False,
@@ -387,6 +393,11 @@ def parse_args():
     parser.add_argument(
         "--num-workers", type=int, default=0, help="The number of processes."
     )
+    parser.add_argument(
+        "--cpu-sampling",
+        action="store_true",
+        help="Disables GPU sampling and utilizes the CPU for dataloading.",
+    )
     return parser.parse_args()