diff --git a/thunder/__init__.py b/thunder/__init__.py
index 104af7734f..5af42c14ed 100644
--- a/thunder/__init__.py
+++ b/thunder/__init__.py
@@ -72,6 +72,7 @@
 from thunder.core.interpreter import print_interpreter_log, print_to_log
 from thunder.core.jit_ext import thunder_general_jit
 from thunder.executors.torch_autograd import split_forward_backward, ThunderFunction
+from thunder.transforms.tensor_subclasses import flatten_tensor_subclasses
 
 # NOTE This import is intentionally pytorch so that it thunder.torch doesn't import this
 import torch as pytorch
@@ -580,6 +581,8 @@ def get_computation_and_inputs(*args, **kwargs):
                     if len(tensor_args_consumed_by_inplace_grouped_by_numel) > 1:
                         vanilla_tensor_args = set(tensor_indices)
 
+            computation_trc = flatten_tensor_subclasses(computation_trc)
+
             if epilogue_trc is not None:
                 epilogue_traces = [epilogue_trc]
             else:
diff --git a/thunder/core/proxies.py b/thunder/core/proxies.py
index 8cf179e263..3c1ae4024e 100644
--- a/thunder/core/proxies.py
+++ b/thunder/core/proxies.py
@@ -2104,8 +2104,8 @@ def tensorproxy(t: torch.Tensor, /, *, name: None | str, history: None | tuple =
     else:
         # NOTE Without tuple(t.shape) then the shape would be a torch.Size object
         shape = tuple(t.shape)
-    return TensorProxy(
-        name,
+    ctor_kwargs = dict(
+        name=name,
         shape=tuple(shape),
         device=device,
         dtype=dtype,
@@ -2116,6 +2116,29 @@ def tensorproxy(t: torch.Tensor, /, *, name: None | str, history: None | tuple =
         thunder_fsdp_padding_size=_thunder_fsdp_padding_size,
     )
 
+    if type(t) not in (torch.Tensor, torch.nn.Parameter) and isinstance(t, torch.Tensor):
+        baseutils.check(
+            hasattr(t, "__tensor_flatten__") and hasattr(t, "__tensor_unflatten__"),
+            lambda: f"{t=} seems to be a tensor subclass but not traceable",
+        )
+        tensor_attr_names, metadata = t.__tensor_flatten__()
+        tensors = [getattr(t, name) for name in tensor_attr_names]
+        ctor_kwargs.update(
+            {
+                "tensors": tensors,
+                "non_tensors": list(metadata.values()),
+                "subclass_type": type(t),
+            }
+        )
+        p = SubclassTensorProxy(**ctor_kwargs)
+        for name, tensor in zip(tensor_attr_names, tensors):
+            setattr(p, name, tensor)
+        for name, value in metadata.items():
+            setattr(p, name, value)
+        return p
+    else:
+        return TensorProxy(**ctor_kwargs)
+
 
 def futuretensorproxy(
     t: torch.Tensor | TensorProxy | FutureTensorProxy, /, *, name: None | str, history: None | tuple = None