diff --git a/thunder/benchmarks/benchmark_litgpt.py b/thunder/benchmarks/benchmark_litgpt.py
index 4d6a7feb6..1437a2222 100644
--- a/thunder/benchmarks/benchmark_litgpt.py
+++ b/thunder/benchmarks/benchmark_litgpt.py
@@ -241,6 +241,7 @@ def __init__(
         use_torchao_fp8_allgather: bool = False,
         use_torchao_fp8_precompute_scale_for_fsdp: bool = False,
         fp8_shard_intermediate_activation: bool = False,
+        enable_compiled_autograd: bool = False,
     ):
         seed = 1337
         torch.manual_seed(seed)
@@ -275,6 +276,7 @@ def __init__(
         self.dump_thunder_traces = dump_thunder_traces
         self.dump_memory_snapshot = dump_memory_snapshot
         self.fp8_shard_intermediate_activation = fp8_shard_intermediate_activation
+        self.enable_compiled_autograd = enable_compiled_autograd
 
         if use_torchao_fp8_linear:
 
@@ -669,11 +671,12 @@ def train(self):
                     input_ids, targets = next(self.train_data_iter)
                     input_ids = input_ids.to(self.device)
                     targets = targets.to(self.device)
-                    if self.use_te_fp8_autocast:
-                        with te.fp8_autocast():
+                    with torch._dynamo.utils.maybe_enable_compiled_autograd(self.enable_compiled_autograd):
+                        if self.use_te_fp8_autocast:
+                            with te.fp8_autocast():
+                                logits = self.model(input_ids)
+                        else:
                             logits = self.model(input_ids)
-                    else:
-                        logits = self.model(input_ids)
                     logits = logits.reshape(-1, logits.size(-1))
                     targets = targets.reshape(-1)
                     loss = (
@@ -685,11 +688,12 @@ def train(self):
             input_ids, targets = next(self.train_data_iter)
             input_ids = input_ids.to(self.device)
             targets = targets.to(self.device)
-            if self.use_te_fp8_autocast:
-                with te.fp8_autocast():
+            with torch._dynamo.utils.maybe_enable_compiled_autograd(self.enable_compiled_autograd):
+                if self.use_te_fp8_autocast:
+                    with te.fp8_autocast():
+                        logits = self.model(input_ids)
+                else:
                     logits = self.model(input_ids)
-            else:
-                logits = self.model(input_ids)
             # This information is accurate only in the case when torch.compile
             # uses a single graph for the entire forward pass In the case of
             # torch.compile using multiple graphs, the saved_tensors will be