diff --git a/aten/src/ATen/TensorIterator.cpp b/aten/src/ATen/TensorIterator.cpp
index dccd2009046..7b1442db75a 100644
--- a/aten/src/ATen/TensorIterator.cpp
+++ b/aten/src/ATen/TensorIterator.cpp
@@ -1491,10 +1491,19 @@ void TensorIteratorBase::build(TensorIteratorConfig& config) {
 
   if (is_meta_) return;
 
+  auto has_storage = true;
+  for (auto& op : operands_) {
+    has_storage &= op.tensor_base().has_storage();
+  }
+  auto privateuse1_without_storage =
+     common_device_.type() == DeviceType::PrivateUse1 &&
+     !has_storage;
+
   // XLA and lazy tensors don't have storage, so they don't have an underlying data pointer.
   // Nothing beyond this point is important for meta functions, so it's fine to exit early here.
   // Extend the condition to ORT tesnors as ORT tensors also don't have storage.
-  if (common_device_.type() == DeviceType::XLA  ||
+  if (privateuse1_without_storage  ||
+      common_device_.type() == DeviceType::XLA  ||
       common_device_.type() == DeviceType::IPU  ||
       common_device_.type() == DeviceType::Lazy ||
       common_device_.type() == DeviceType::ORT  ||
diff --git a/torch/_tensor.py b/torch/_tensor.py
index 05171c7ae0e..c7cb9b338b9 100644
--- a/torch/_tensor.py
+++ b/torch/_tensor.py
@@ -116,6 +116,7 @@ class Tensor(torch._C._TensorBase):
             if (
                 self.is_sparse
                 or self.device.type in ["lazy", "xla", "mps", "ort", "meta", "hpu"]
+                or (self.storage is None and self.device.type == "privateuseone")
                 or (type(self) is not Tensor and self.data_ptr() == 0)
             ):
                 new_tensor = self.clone()
@@ -271,7 +272,9 @@ class Tensor(torch._C._TensorBase):
         # 2. Python list is not a good fit due to performance reason.
         #    `tolist()` converts every single element in the tensor into python objects
         #    and serialize them one by one.
-        if self.device.type in ["xla", "ort", "hpu"]:
+        if self.device.type in ["xla", "ort", "hpu"] or (
+            self.storage is None and self.device.type == "privateuseone"
+        ):
             # Convert BFloat16 tesors to Float32 before conversion to numpy, as numpy doesn't
             # support BFloat16. The rebuild tensor from numpy takes in the original self.dtype,
             # this would reconstruct the BFloat16 tensor from numpy.