diff --git a/thunder/tests/test_inplace_functionalization.py b/thunder/tests/test_inplace_functionalization.py
index 0eb591c2f8..0e005fe4b5 100644
--- a/thunder/tests/test_inplace_functionalization.py
+++ b/thunder/tests/test_inplace_functionalization.py
@@ -2,6 +2,7 @@
 from collections.abc import Callable
 from dataclasses import dataclass
 from functools import partial
+from typing import TYPE_CHECKING
 
 import pytest
 import torch.testing
@@ -13,6 +14,9 @@
 from thunder.tests.make_tensor import make_tensor
 from thunder.torch import _torch_to_thunder_function_map, _inplace_to_out_of_place
 
+if TYPE_CHECKING:
+    from thunder.core.symbol import Symbol
+
 
 # `SampleInput`s of ops with `inplace` argument do not seem to come with `inplace` arg, so give it to them.
 def sample_generator_wrapper(sample_generator):
@@ -36,7 +40,7 @@ def inplace_masked_fill_sample_generator(op, device, dtype, requires_grad, **kwa
         yield SampleInput(a, pred, value)
 
 
-_torchsymbol_to_torch: dict[Sybmol, Callable] = {v: k for k, v in _torch_to_thunder_function_map.items()}
+_torchsymbol_to_torch: dict[Symbol, Callable] = {v: k for k, v in _torch_to_thunder_function_map.items()}
 _functional_to_inplace: dict[Callable, Callable] = {
     functional: inplace for inplace, (functional, index) in _inplace_to_out_of_place.items() if index == -1
 }
@@ -125,33 +129,39 @@ def test_functionalization(op: OpInfo, device: str, dtype: dtypes.dtype, executo
     )
 
 
-# TODO(crcrpar): Investigate the numerical accuracy when `train=True` and dtype is fp32.
-# with RTX6000 Ada and CUDA 12.3, I see somewhat huge error:
-# E   AssertionError: Tensor-likes are not close!
-# E
-# E   Mismatched elements: 913 / 1000 (91.3%)
-# E   Greatest absolute difference: 0.000273287296295166 at index (0, 50) (up to 1e-05 allowed)
-# E   Greatest relative difference: 0.4177769422531128 at index (0, 727) (up to 1.3e-06 allowed)
+@pytest.fixture
+def turn_off_tf32_and_set_seed(monkeypatch):
+    import torch
+
+    monkeypatch.setenv("NVIDIA_TF32_OVERRIDE", "0")
+    torch.manual_seed(42)
+
+
 @requiresCUDA
 @pytest.mark.parametrize("train", (False, True))
-def test_parse_resnet18(train: bool):
+def test_parse_resnet18(turn_off_tf32_and_set_seed, train: bool):
+    from contextlib import nullcontext
     import thunder
 
     torchvision = pytest.importorskip("torchvision")
 
     device = torch.device("cuda")
-    dtype = torch.float64 if train else torch.float32
+    dtype = torch.float32
     with device:
-        model: nn.Module = torchvision.models.resnet18(weights=None).to(device=device, dtype=dtype)
-        ref_model: nn.Module = torchvision.models.resnet18(weights=None).to(device=device, dtype=dtype)
+        model = torchvision.models.resnet18(weights=None).to(device=device, dtype=dtype)
+        ref_model = torchvision.models.resnet18(weights=None).to(device=device, dtype=dtype)
     if not train:
         model = model.eval()
         ref_model = ref_model.eval()
+        ctx = torch.no_grad
+    else:
+        ctx = nullcontext
     ref_model.load_state_dict(model.state_dict())
 
     jitted = thunder.jit(model)
     x = make_tensor((1, 3, 224, 224), dtype=dtype, device=device)
-    torch.testing.assert_close(jitted(x), ref_model(x))
+    with ctx():
+        torch.testing.assert_close(jitted(x), ref_model(x))
 
 
 @instantiate(