diff --git a/thunder/core/rematerialization.py b/thunder/core/rematerialization.py
index 7db7b88f64..3c64737ee6 100644
--- a/thunder/core/rematerialization.py
+++ b/thunder/core/rematerialization.py
@@ -163,6 +163,11 @@ def apply_rematerialization_for_consumer(
         filter(lambda x: x.name not in map(lambda x: x.name, new_consumer_args), consumer.args)
     )
 
+    # In the case where there are no tensors to rematerialize it is
+    # possible to terminate early and return the consumer as it was.
+    if not rematerialized_inputs:
+        return consumer
+
     # Construct a temporary Trace object with subsymbols from the producer.
     trace = TraceCtx(None)
     trace.bound_symbols = producer.subsymbols
diff --git a/thunder/tests/test_nvfuser.py b/thunder/tests/test_nvfuser.py
index 2224bfc9ba..cb4ece6aa0 100644
--- a/thunder/tests/test_nvfuser.py
+++ b/thunder/tests/test_nvfuser.py
@@ -210,7 +210,7 @@ def foo(a, x):
     assert len(fusions[0].subsymbols) == 3
 
     # Verifies the intermediate consumer
-    assert fusions[1].subsymbols[-2].args[0].name == "g"
+    assert fusions[1].subsymbols[-1].args[0].name == "g"
 
 
 @instantiate(executors=(nvFuserExecutor,), dtypes=(thunder.float32,))
diff --git a/thunder/tests/test_nvfuser_remat.py b/thunder/tests/test_nvfuser_remat.py
index d0f3462391..d5b57320fa 100644
--- a/thunder/tests/test_nvfuser_remat.py
+++ b/thunder/tests/test_nvfuser_remat.py
@@ -19,6 +19,7 @@
 from thunder.examine import get_fusions
 from thunder.tests.framework import instantiate, NOTHING, nvFuserExecutor, TorchExecutor, requiresCUDA
 from thunder.tests.make_tensor import make_tensor
+import thunder.torch as ltorch
 
 
 @value_and_grad
@@ -199,6 +200,40 @@ def test_apply_rematerialization_consumer(executor, device, _):
     assert tuple(new_consumer.subsymbols) == tuple(new_consumer_case2.subsymbols)
 
 
+@instantiate(
+    dtypes=NOTHING,
+    executors=(nvFuserExecutor,),
+)
+@disable_rematerialization_in_nvfuser_fusion
+def test_apply_rematerialization_consumer_early_exit(executor, device, _):
+    @value_and_grad
+    def foo(t0):
+        t1 = ttorch.exp(t0)
+        t2 = ttorch.matmul(t1, t1)
+        return t2
+
+    t0 = make_tensor(2, 2, dtype=torch.float32, device=device)
+    initial_trace = thunder.trace()(foo, t0)
+    compiled_func = thunder.jit(initial_trace.python_callable())
+    _ = compiled_func(t0)
+    traces = thunder.last_traces(compiled_func)
+    trace = traces[-1]
+    nvfuser_symbols = tuple(filter(lambda x: x.sym.name.startswith("nvFusion"), trace.bound_symbols))
+    assert len(nvfuser_symbols) == 2
+
+    producer = nvfuser_symbols[0]
+    consumer = nvfuser_symbols[1]
+
+    # Create a cut that has t0 as extra information and
+    # that contains all arguments(t2) from consumer.
+    cut = ("t0", "t2")
+    new_consumer = apply_rematerialization_for_consumer(producer, consumer, cut)
+
+    # Check that the new consumer is the old consumer
+    assert id(new_consumer) == id(consumer)
+    assert tuple(new_consumer.subsymbols) == tuple(consumer.subsymbols)
+
+
 @instantiate(
     dtypes=NOTHING,
     executors=(nvFuserExecutor,),