diff --git a/test/dynamo/test_repros.py b/test/dynamo/test_repros.py
index 46f0e67a5b7b..e9cc4e5026f5 100644
--- a/test/dynamo/test_repros.py
+++ b/test/dynamo/test_repros.py
@@ -4822,6 +4822,67 @@ class ReproTests(torch._dynamo.test_case.TestCase):
             "encountered a mutation on a view chain of length 2, where view 1 was an as_strided",
         ):
             f_compiled(a)
+        # See https://github.com/pytorch/pytorch/issues/161010
+
+    def test_preserve_stride_with_clone(self) -> None:
+        A = torch.rand(5, 5, device="cuda" if torch.cuda.is_available() else "cpu")
+        B = torch.rand(5, 5, device="cuda" if torch.cuda.is_available() else "cpu")
+
+        def fn(
+            src: torch.Tensor, count: torch.Tensor
+        ) -> tuple[tuple[int, ...], tuple[int, ...]]:
+            Q, R = torch.linalg.qr(src)
+            rhs = torch.ones(Q.shape[0], 1, device=src.device)
+            a = torch.linalg.solve_triangular(R, Q.T @ rhs, upper=True)
+            cloned = a.clone(memory_format=torch.preserve_format)
+            return a.stride(), cloned.stride()
+
+        a_stride, cloned_stride = fn(A, torch.zeros(1))
+        self.assertEqual(
+            a_stride,
+            cloned_stride,
+            f"Strides should match in eager: {a_stride} against {cloned_stride}",
+        )
+
+        compiled_a_stride, compiled_cloned_stride = torch.compile(fn, backend="eager")(
+            B, torch.zeros(1)
+        )
+        self.assertEqual(
+            compiled_a_stride,
+            compiled_cloned_stride,
+            f"Strides should match in eager: {compiled_a_stride} against {compiled_cloned_stride}",
+        )
+
+    # Extension of https://github.com/pytorch/pytorch/issues/161010
+    # in the non memory dense case
+    def test_clone_not_memory_dense(self):
+        def foo() -> torch.Tensor:
+            x = torch.randn(10, 8).t()[::2, ::2]
+            y = x.clone()
+            return y
+
+        y = foo()
+        self.assertEqual(
+            y.stride(),
+            (1, 4),
+            "Reference eager implementation should have stride (1, 4)",
+        )
+        y = torch.compile(foo, backend="eager")()
+        self.assertEqual(
+            y.stride(), (1, 4), "Compile with eager backend should have stride (1, 4)"
+        )
+        y = torch.compile(foo, backend="aot_eager")()
+        self.assertEqual(
+            y.stride(),
+            (1, 4),
+            "Compile with aot_eager backend should have stride (1, 4)",
+        )
+        y = torch.compile(foo, backend="inductor")()
+        self.assertEqual(
+            y.stride(),
+            (1, 4),
+            "Compile with inductor backend should have stride (1, 4)",
+        )
 
     # https://github.com/pytorch/pytorch/issues/146598
     @unittest.expectedFailure
diff --git a/test/test_prims.py b/test/test_prims.py
index 58ed8a7dd758..e528a1eb2e4e 100644
--- a/test/test_prims.py
+++ b/test/test_prims.py
@@ -342,6 +342,16 @@ $1: f32[2] = torch._ops.prims.sin.default($0)""")
             x = torch.randn(4, dtype=torch.complex64, device='meta').conj()
             x + 1
 
+    def test_clone_meta_stride_preservation_dense(self):
+        tensor = torch.randn(1, 5).t()
+        meta_clone = prims._clone_meta(tensor, memory_format=torch.preserve_format)
+        self.assertEqual(tensor.stride(), meta_clone.stride())
+
+    def test_clone_meta_stride_preservation_sparse(self):
+        tensor = torch.arange(12).float().view(3, 4)[1:, ::2]
+        meta_clone = prims._clone_meta(tensor, memory_format=torch.preserve_format)
+        self.assertEqual(tensor.contiguous().stride(), meta_clone.stride())
+
     def test_check_deprecation_warning(self):
         with self.assertWarnsRegex(FutureWarning, 'will be removed in the future'):
             torch._prims_common.check(True, lambda: 'message')
diff --git a/torch/_prims/__init__.py b/torch/_prims/__init__.py
index 5fef517dc59f..034263ea4849 100644
--- a/torch/_prims/__init__.py
+++ b/torch/_prims/__init__.py
@@ -692,16 +692,22 @@ def _clone_meta(
             device=input.device,
             memory_format=memory_format,
         )
+    else:
+        # Match eager behavior by preserving strides for non_overlapping_and_dense tensors
+        # If not, eager clone creates contiguous strides
+        computed_stride = None
+        if utils.is_non_overlapping_and_dense(input):
+            computed_stride = input.stride()
+        else:
+            computed_stride = utils.compute_elementwise_output_strides(input)
 
-    # memory_format == torch.preserve_format
-    strides = utils.compute_elementwise_output_strides(input)
-    return torch.empty_strided(
-        input.shape,
-        strides,
-        dtype=input.dtype,
-        layout=input.layout,
-        device=input.device,
-    )
+        return torch.empty_strided(
+            input.shape,
+            computed_stride,
+            dtype=input.dtype,
+            layout=input.layout,
+            device=input.device,
+        )
 
 
 clone = _make_prim(