diff --git a/torchao/dtypes/nf4tensor.py b/torchao/dtypes/nf4tensor.py
index df2b1f08d..56feb1857 100644
--- a/torchao/dtypes/nf4tensor.py
+++ b/torchao/dtypes/nf4tensor.py
@@ -410,6 +410,7 @@ def get_block_absmax(inpt_tensor: torch.Tensor, block_size: int) -> torch.Tensor
 class NF4Tensor(torch.Tensor):
     """NF4Tensor class for converting a weight to the QLoRA NF4 format"""
 
+    @torch._dynamo.disable
     def __new__(
         cls,
         # Args related for base tensor construction
@@ -450,6 +451,7 @@ def __new__(
         )
         return nf4tensor
 
+    @torch._dynamo.disable
     def __init__(
         self,
         tensor_meta: SubclassTensorArgs,
@@ -758,6 +760,7 @@ def __str__(self):
         return self.to(torch.float32).__str__()
 
     @classmethod
+    @torch._dynamo.disable
     def __torch_dispatch__(cls, func, types, args, kwargs=None):
         """TODO we are not supporting torch dispatch at the moment
         instead we have created a Autograd.Function to handle the linear
@@ -849,7 +852,7 @@ def fsdp_post_all_gather(
             ), f"Expects out's data to be the all-gather output"
             return
 
-        return NF4Tensor(
+        return nf4_constructor(
             tensor_meta,
             block_size,
             n_blocks,
@@ -934,3 +937,27 @@ def function_cpu(*args, **kwargs):
     updated_attrs = call_from_inner_tensors(nf4tensor, "cpu", args[1:], kwargs)
     updated_attrs["device"] = "cpu"
     return NF4Tensor(*construct_nf4_args(nf4tensor, updated_attrs))
+
+@torch._dynamo.allow_in_graph
+def nf4_constructor(
+    tensor_meta: SubclassTensorArgs,
+    block_size: int,
+    n_blocks: int,
+    scaler_block_size: int,
+    quantized_scalers: torch.Tensor,
+    quantization_factor: torch.Tensor,
+    scaler_mean: torch.Tensor,
+    quantized_data: torch.Tensor,
+    nf4: torch.Tensor,
+):
+    return NF4Tensor(
+        tensor_meta,
+        block_size,
+        n_blocks,
+        scaler_block_size,
+        quantized_scalers,
+        quantization_factor,
+        scaler_mean,
+        quantized_data,
+        nf4,
+    )