diff --git a/python/sglang/srt/managers/mm_utils.py b/python/sglang/srt/managers/mm_utils.py
index 6c790543a..77f26c959 100644
--- a/python/sglang/srt/managers/mm_utils.py
+++ b/python/sglang/srt/managers/mm_utils.py
@@ -1509,7 +1509,7 @@ def get_new_expanded_mm_items(original_mm_items):
                 total_feature_len = feature_len
                 for i in range(num_items):
                     start, end = slice_indices[i], slice_indices[i + 1]
-                    new_item = copy.deepcopy(item)
+                    new_item = copy.copy(item)
                     if item.feature is not None:
                         new_item.feature = _slice_value(item.feature, start, end)
                     if item.precomputed_embeddings is not None:
@@ -1600,7 +1600,7 @@ def get_new_expanded_mm_items(original_mm_items):
                         frame_start_indices[video_idx + 1],
                     )
 
-                    new_item = copy.deepcopy(item)
+                    new_item = copy.copy(item)
                     if item.feature is not None:
                         new_item.feature = _slice_value(item.feature, start, end)
                     if item.precomputed_embeddings is not None:
diff --git a/python/sglang/srt/managers/schedule_batch.py b/python/sglang/srt/managers/schedule_batch.py
index 6a019496a..9cd6e4b89 100644
--- a/python/sglang/srt/managers/schedule_batch.py
+++ b/python/sglang/srt/managers/schedule_batch.py
@@ -336,6 +336,28 @@ class MultimodalDataItem:
         self.hash = hash((self.hash, other.hash))
         self.set_pad_value()
 
+    def reconstruct(self):
+        if not isinstance(self.feature, CudaIpcTensorTransportProxy):
+            return
+
+        reconstruct_device = torch.cuda.current_device()
+        if isinstance(self.feature, CudaIpcTensorTransportProxy):
+            self.feature = self.feature.reconstruct_on_target_device(reconstruct_device)
+        if isinstance(self.precomputed_embeddings, CudaIpcTensorTransportProxy):
+            self.precomputed_embeddings = (
+                self.precomputed_embeddings.reconstruct_on_target_device(
+                    reconstruct_device
+                )
+            )
+        for extra_key in self.model_specific_data:
+            if isinstance(
+                self.model_specific_data[extra_key], CudaIpcTensorTransportProxy
+            ):
+                extra_data = self.model_specific_data[
+                    extra_key
+                ].reconstruct_on_target_device(reconstruct_device)
+                self.model_specific_data[extra_key] = extra_data
+
 
 @dataclasses.dataclass
 class MultimodalInputs:
@@ -373,13 +395,16 @@ class MultimodalInputs:
 
     @staticmethod
     def from_dict(obj: dict):
+        original_mm_items = obj["mm_items"]
+        for mm_item in original_mm_items:
+            mm_item.reconstruct()
+
         # Check if MM splitting is enabled
         if not envs.SGLANG_ENABLE_MM_SPLITTING.get():
-            mm_items = obj["mm_items"]
+            mm_items = original_mm_items
         else:
             from sglang.srt.managers.mm_utils import get_new_expanded_mm_items
 
-            original_mm_items = obj["mm_items"]
             # Now, `mm_items` contains one item per image.
             mm_items = get_new_expanded_mm_items(original_mm_items)
 
@@ -1673,13 +1698,6 @@ class ScheduleBatch(ScheduleBatchDisaggregationDecodeMixin):
                 pixel_values = getattr(mm_item, "feature", None)
                 if isinstance(pixel_values, torch.Tensor):
                     mm_item.feature = pixel_values.to(self.device, non_blocking=True)
-                elif isinstance(pixel_values, CudaIpcTensorTransportProxy):
-                    mm_item.feature = pixel_values.reconstruct_on_target_device(
-                        torch.cuda.current_device()
-                    )
-                    # The reference by CudaIpcTensorTransportProxy was cut off,
-                    # proactively delete to avoid slow gc.
-                    del pixel_values
                 if get_global_server_args().language_only:
                     precomputed_embeddings = getattr(
                         mm_item, "precomputed_embeddings", None
diff --git a/python/sglang/test/test_mm_utils.py b/python/sglang/test/test_mm_utils.py
new file mode 100644
index 000000000..1d8585417
--- /dev/null
+++ b/python/sglang/test/test_mm_utils.py
@@ -0,0 +1,50 @@
+import unittest
+from unittest.mock import Mock, patch
+
+import torch
+
+from sglang.srt.managers import mm_utils, schedule_batch
+from sglang.srt.managers.schedule_batch import (
+    Modality,
+    MultimodalDataItem,
+    MultimodalInputs,
+)
+
+
+def _make_proxy_with_reconstruct_result(tensor: torch.Tensor):
+    proxy = mm_utils.CudaIpcTensorTransportProxy.__new__(
+        mm_utils.CudaIpcTensorTransportProxy
+    )
+    proxy.reconstruct_on_target_device = Mock(return_value=tensor)
+    return proxy
+
+
+class TestMultimodalInputsFromDict(unittest.TestCase):
+    def test_materialize_proxy(self):
+        feature_tensor = torch.tensor([[7.0], [8.0]], dtype=torch.float32)
+        proxy_feature = _make_proxy_with_reconstruct_result(feature_tensor)
+        mm_item = MultimodalDataItem(
+            modality=Modality.IMAGE,
+            offsets=[(0, 1), (1, 2)],
+            feature=proxy_feature,
+            model_specific_data={"image_grid_thw": [[1, 1, 1], [1, 1, 1]]},
+        )
+
+        with patch.object(
+            schedule_batch.torch.cuda, "is_available", return_value=True
+        ), patch.object(
+            schedule_batch.torch.cuda, "current_device", return_value=0
+        ), patch.object(
+            schedule_batch.envs.SGLANG_ENABLE_MM_SPLITTING, "get", return_value=False
+        ), patch.object(
+            schedule_batch.envs.SGLANG_MM_BUFFER_SIZE_MB, "get", return_value=0
+        ):
+            mm_inputs = MultimodalInputs.from_dict({"mm_items": [mm_item]})
+
+        self.assertEqual(len(mm_inputs.mm_items), 1)
+        self.assertTrue(torch.equal(mm_inputs.mm_items[0].feature, feature_tensor))
+        proxy_feature.reconstruct_on_target_device.assert_called_once_with(0)
+
+
+if __name__ == "__main__":
+    unittest.main(verbosity=2)