diff --git a/dpnp/tests/third_party/cupy/fft_tests/test_fft.py b/dpnp/tests/third_party/cupy/fft_tests/test_fft.py
index 2de6137ec748..3c0f9b3ad52a 100644
--- a/dpnp/tests/third_party/cupy/fft_tests/test_fft.py
+++ b/dpnp/tests/third_party/cupy/fft_tests/test_fft.py
@@ -1,13 +1,14 @@
 from __future__ import annotations
 
 import functools
+import math
 import warnings
 
 import numpy as np
 import pytest
 
 import dpnp as cupy
-from dpnp.tests.helper import has_support_aspect64
+from dpnp.tests.helper import get_float_dtypes, has_support_aspect64
 
 # from cupy.fft import config
 # from cupy.fft._fft import (
@@ -1288,6 +1289,45 @@ def test_irfftn(self, xp, dtype, order, enable_nd):
         return xp.fft.irfftn(a, s=self.s, axes=self.axes, norm=self.norm)
 
 
+@pytest.mark.parametrize("dtype", get_float_dtypes())
+@pytest.mark.parametrize("offset", [0, 1])
+@pytest.mark.parametrize("ndim", [1, 3])
+@pytest.mark.parametrize("layout", ["view", "strided", "pointer"])
+def test_rfft_input_alignment(
+    dtype: type[np.float32 | np.float64], offset: int, ndim: int, layout: str
+) -> None:
+    shape: tuple[int, ...] = (35,) * ndim
+    backing: cupy.ndarray
+    x: cupy.ndarray
+    if layout == "view":
+        backing = cupy.ones(shape=(2,) + shape, dtype=dtype)
+        x = backing[offset]
+    elif layout == "strided":
+        backing = cupy.ones(shape=shape[:-1] + (2 * shape[-1],), dtype=dtype)
+        x = backing[..., offset::2]
+    else:
+        backing = cupy.ones(shape=math.prod(shape) + 1, dtype=dtype)
+        x = cupy.ndarray(shape, dtype=dtype, buffer=backing, offset=offset)
+    assert x.data.ptr % (2 * x.itemsize) == offset * x.itemsize
+
+    out: cupy.ndarray = cupy.fft.rfft(x) if ndim == 1 else cupy.fft.rfftn(x)
+    expected: np.ndarray = np.fft.rfftn(np.ones(shape=shape, dtype=dtype))
+    # oneMKL f32 err in zero bins scales with DC value (prod(shape))
+    testing.assert_allclose(
+        actual=out,
+        desired=expected,
+        rtol=1e-5 if dtype is np.float32 else 1e-12,
+        atol=(
+            np.finfo(dtype).eps * math.prod(shape)
+            if dtype is np.float32
+            else 1e-9
+        ),
+    )
+    testing.assert_array_equal(
+        actual=backing, desired=np.ones(shape=backing.shape, dtype=dtype)
+    )
+
+
 # Only those tests in which a legit plan can be obtained are kept
 @testing.with_requires("numpy>=2.0")
 @pytest.mark.usefixtures("skip_forward_backward")