diff --git a/tripy/tests/integration/test_conv_transpose.py b/tripy/tests/integration/test_conv_transpose.py
index 816fe6b7f..40f34a906 100644
--- a/tripy/tests/integration/test_conv_transpose.py
+++ b/tripy/tests/integration/test_conv_transpose.py
@@ -280,14 +280,13 @@ def test_transposed_equivalency(self, torch_dtype, tp_dtype, eager_or_compiled):
         output = eager_or_compiled(conv_layer, input)
         output_transpose = eager_or_compiled(conv_transpose_layer, input)
 
-        rtol = 2e-7 if tp_dtype == tp.float32 else 9e-4
-        assert tp.allclose(output, tp.Tensor(expected), rtol=rtol, atol=1e-5)
+        assert tp.allclose(output, tp.Tensor(expected), rtol=1e-2, atol=1e-4)
         assert output.shape == list(expected.shape)
-        assert tp.allclose(output_transpose, tp.Tensor(expected_transpose), rtol=rtol, atol=1e-5)
+        assert tp.allclose(output_transpose, tp.Tensor(expected_transpose), rtol=1e-2, atol=1e-4)
         assert output_transpose.shape == list(expected_transpose.shape)
-        assert tp.allclose(output, output_transpose, rtol=rtol, atol=1e-5)
+        assert tp.allclose(output, output_transpose, rtol=1e-2, atol=1e-4)
         assert output.shape == output_transpose.shape
-        assert tp.allclose(tp.Tensor(expected), tp.Tensor(expected_transpose), rtol=rtol, atol=1e-5)
+        assert tp.allclose(tp.Tensor(expected), tp.Tensor(expected_transpose), rtol=1e-2, atol=1e-4)
         assert list(expected.shape) == list(expected_transpose.shape)
 
     @pytest.mark.parametrize("test_case", test_cases_transpose_downscale)
diff --git a/tripy/tests/integration/test_sequential.py b/tripy/tests/integration/test_sequential.py
index 812951e13..b6f5e9c71 100644
--- a/tripy/tests/integration/test_sequential.py
+++ b/tripy/tests/integration/test_sequential.py
@@ -102,8 +102,7 @@ def test_nested_forward_pass_accuracy(self, eager_or_compiled):
         with torch.no_grad():
             torch_output = torch_model(input_tensor)
 
-        rtol_ = 2e-6
-        assert torch.allclose(torch.from_dlpack(tp_output), torch_output, rtol=rtol_)
+        assert torch.allclose(torch.from_dlpack(tp_output), torch_output, rtol=1e-4, atol=1e-4)
 
     def test_basic_state_dict_comparison(self):
         torch_model = torch.nn.Sequential(