diff --git a/torch/_tensor.py b/torch/_tensor.py
index 26935a6a60a..652cd33a035 100644
--- a/torch/_tensor.py
+++ b/torch/_tensor.py
@@ -330,7 +330,7 @@ class Tensor(torch._C.TensorBase):
             torch.serialization._serialization_tls.materialize_fake_tensors
         )
 
-        if self.device.type in ["xla", "maia"] or (
+        if self.device.type in ["xla", "maia", "mtia"] or (
             not torch._C._has_storage(self)
             and self.device.type == torch._C._get_privateuse1_backend_name()
         ):
@@ -343,34 +343,6 @@ class Tensor(torch._C.TensorBase):
                 torch._utils._rebuild_device_tensor_from_cpu_tensor,
                 (cpu_tensor, self.dtype, str(self.device), self.requires_grad),
             )
-        # Legacy comment that does not hold anymore.
-        # Note: Numpy array is chosen to be the rebuild component for XLA, MTIA, MAIA Tensors.
-        # We considered a few options:
-        # 1. CPU tensor can't be used here.
-        #    Otherwise in torch.load CPU storage is reconstructed with randomly
-        #    initialized data, moved onto backend device, and then storage is updated
-        #    to the serialized content. This works perfectly for CPU/CUDA but not these backends;
-        #    their tensors are disconnected with storage so they don't get the update.
-        # 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 ["mtia"]:
-            # 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.
-            if skip_data:
-                raise RuntimeError(
-                    "Cannot serialize tensors on backends with no storage under skip_data context manager"
-                )
-            numpy_tensor = (
-                self.cpu().numpy()
-                if self.dtype != torch.bfloat16
-                else self.cpu().to(torch.float32).numpy()
-            )
-            return (
-                torch._utils._rebuild_device_tensor_from_numpy,
-                (numpy_tensor, self.dtype, str(self.device), self.requires_grad),
-            )
         if self.device.type == "meta":
             # NB: This implementation BREAKS storage sharing.  Current
             # hypothesis is that no one cares for meta tensors.