diff --git a/test/test_dlpack.py b/test/test_dlpack.py
index a9036be160b..fe1107ac850 100644
--- a/test/test_dlpack.py
+++ b/test/test_dlpack.py
@@ -15,6 +15,23 @@ from torch.testing._internal.common_utils import IS_JETSON, run_tests, TestCase
 from torch.utils.dlpack import from_dlpack, to_dlpack
 
 
+# Wraps a tensor, exposing only DLPack methods:
+#    - __dlpack__
+#    - __dlpack_device__
+#
+# This is used for guaranteeing we are going through the DLPack method, and not
+# something else, e.g.: CUDA array interface, buffer protocol, etc.
+class TensorDLPackWrapper:
+    def __init__(self, tensor):
+        self.tensor = tensor
+
+    def __dlpack__(self, *args, **kwargs):
+        return self.tensor.__dlpack__(*args, **kwargs)
+
+    def __dlpack_device__(self, *args, **kwargs):
+        return self.tensor.__dlpack_device__(*args, **kwargs)
+
+
 class TestTorchDlPack(TestCase):
     exact_dtype = True
 
@@ -251,6 +268,19 @@ class TestTorchDlPack(TestCase):
         # gh-83069, make sure __dlpack__ normalizes strides
         self.assertEqual(z.stride(), (1,))
 
+    @skipMeta
+    @onlyNativeDeviceTypes
+    def test_automatically_select_in_creation(self, device):
+        # Create a new tensor, and wrap it using TensorDLPackWrapper.
+        tensor = torch.rand(10)
+        wrap = TensorDLPackWrapper(tensor)
+        # Create a new tensor from the wrapper.
+        # This should identify that the wrapper class provides the DLPack methods
+        # and use them for creating the new tensor, instead of iterating element
+        # by element.
+        new_tensor = torch.tensor(wrap)
+        self.assertEqual(tensor, new_tensor)
+
 
 instantiate_device_type_tests(TestTorchDlPack, globals())
 
diff --git a/torch/csrc/utils/tensor_new.cpp b/torch/csrc/utils/tensor_new.cpp
index 099991f8414..e6371498314 100644
--- a/torch/csrc/utils/tensor_new.cpp
+++ b/torch/csrc/utils/tensor_new.cpp
@@ -345,6 +345,23 @@ Tensor internal_new_from_data(
   }
 #endif
 
+  if (PyObject_HasAttrString(data, "__dlpack__")) {
+    py::object tensor_o =
+        py::module::import("torch").attr("utils").attr("dlpack").attr(
+            "from_dlpack")(py::handle(data));
+    Tensor tensor = py::cast<Tensor>(tensor_o);
+    const auto& inferred_scalar_type =
+        type_inference ? tensor.scalar_type() : scalar_type;
+    auto device = device_opt.has_value() ? *device_opt : tensor.device();
+    pybind11::gil_scoped_release no_gil;
+    maybe_initialize_device(device);
+    return tensor.to(
+        device,
+        inferred_scalar_type,
+        /*non_blocking=*/false,
+        /*copy=*/copy_variables);
+  }
+
   auto device = device_opt.has_value() ? *device_opt : options.device();
 
   auto sizes = compute_sizes(data, scalar_type);