diff --git a/thunder/tests/test_autocast.py b/thunder/tests/test_autocast.py
index 1d80bf583..7c1b647a5 100644
--- a/thunder/tests/test_autocast.py
+++ b/thunder/tests/test_autocast.py
@@ -27,6 +27,7 @@ def test_thunder_autocast_transform(executor, device, dtype):
     def f(a, b, c):
         return a @ (b + c)
 
+    # The following functions needs to be updated as autocast_impls grows.
     def g(a, b, c):
         return a + b - c
 
@@ -58,6 +59,7 @@ def h(a, b, c):
         out = compiled(x, y, z)
 
         devicetype = torch.device(device).type
+        # note(crcrpar): This test could be broken in the future as thunder autocast develops.
         with torch.autocast(device_type=devicetype, dtype=autocast_torch_dtype):
             torch_output = func(x, y, z)
         assert out.dtype == torch_output.dtype
@@ -309,22 +311,3 @@ def foo(a, b, c, d):
 
         for eg, jg in zip(eager_grads, jit_grads):
             torch.testing.assert_close(eg, jg, rtol=5e-3, atol=5e-3)
-
-
-# def simple_addition(x, y):
-#     return x + y
-
-
-# def test_autocast_transform():
-#     autocast_transform = AutocastTransform(dtype=torch.bfloat16)
-#     jitted_fn = jit(simple_addition, transforms=[autocast_transform])
-
-#     x = torch.randn(2, 2, dtype=torch.float32)
-#     y = torch.randn(2, 2, dtype=torch.float32)
-
-#     result = jitted_fn(x, y)
-
-#     assert result.dtype == torch.bfloat16, f"Expected dtype: bfloat16, but got: {result.dtype}"
-
-#     expected_result = simple_addition(x, y).to(torch.bfloat16)
-#     assert torch.allclose(result, expected_result), "The output values do not match the expected results."
diff --git a/thunder/transforms/autocast.py b/thunder/transforms/autocast.py
index 0d860cabc..545f731e2 100644
--- a/thunder/transforms/autocast.py
+++ b/thunder/transforms/autocast.py
@@ -347,55 +347,40 @@ def __init__(self, trace, dtype, *args, **kwargs):
                 self.dtype = dtype
 
             def process_bsym(self, bsym):
-                # Skip special symbols that shouldn't be processed
                 if bsym.sym.id in trace_interpreter_skip_list:
                     self.new_trace.bound_symbols.append(bsym.from_bsym())
                     return
 
-                # Check if symbol has an autocast implementation
                 autocast_impl = _maybe_get_autocast_rule_for_symbol(bsym.sym)
 
                 if autocast_impl is not None:
-                    # Read the arguments with potential autocast conversion
                     args = tree_map(self.read, bsym.args)
                     kwargs = tree_map(self.read, bsym.kwargs)
 
-                    # Apply the autocast implementation
                     with disable_autocast():
                         result = autocast_impl(*args, **kwargs, dtype=self.dtype)
 
                     self.set_result(result)
                 else:
-                    # No autocast rule, process normally
                     args = tree_map(self.read, bsym.args)
                     kwargs = tree_map(self.read, bsym.kwargs)
                     result = bsym.sym(*args, **kwargs)
                     self.set_result(result)
 
-                    # Add the bound symbol to new trace
                     new_bsym = bsym.from_bsym()
                     new_bsym.args = args
                     new_bsym.kwargs = kwargs
                     self.add_processed_bsyms([new_bsym])
 
-        # Process the computation trace
         if computation_trace is not None:
             processor = AutocastProcessor(computation_trace, self.dtype)
 
-            # Get the actual args and kwargs from the kwargs dict
             args = kwargs.get("args", ())
             kw = kwargs.get("kwargs", {})
 
-            with tracectx(processor.new_trace):
-                # Initialize the processor's environment with input arguments
-                for trace_arg, arg in zip(computation_trace.args, args):
-                    processor.env[trace_arg.name] = arg
+            processor.process_args(*args, **kw)
 
-                # Initialize kwargs if any
-                for trace_kwarg, kwarg in zip(computation_trace.kwargs.values(), kw.values()):
-                    processor.env[trace_kwarg.name] = kwarg
-
-                new_trace, _ = processor()
-                computation_trace = new_trace
+            new_trace, outputs = processor()
+            computation_trace = new_trace
 
         return prologue_trace, computation_trace, epilogue_trace