diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index 3ce603c3ed2..5d87646eba3 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -21,6 +21,7 @@
 import torchvision.transforms.v2 as transforms
 
 from common_utils import (
+    assert_close,
     assert_equal,
     cache,
     cpu_and_cuda,
@@ -41,7 +42,6 @@
 )
 
 from torch import nn
-from torch.testing import assert_close
 from torch.utils._pytree import tree_flatten, tree_map
 from torch.utils.data import DataLoader, default_collate
 from torchvision import tv_tensors
@@ -4074,14 +4074,28 @@ def test_kernel_uint8(self, make_input):
 
     @pytest.mark.parametrize(
         "make_input",
-        [make_image_tensor, make_image, make_video],
+        [
+            make_image_tensor,
+            make_image,
+            make_video,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CV-CUDA not available")
+            ),
+        ],
     )
     def test_functional_float(self, make_input):
         check_functional(F.gaussian_noise, make_input(dtype=torch.float32))
 
     @pytest.mark.parametrize(
         "make_input",
-        [make_image_tensor, make_image, make_video],
+        [
+            make_image_tensor,
+            make_image,
+            make_video,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CV-CUDA not available")
+            ),
+        ],
     )
     def test_functional_uint8(self, make_input):
         check_functional(F.gaussian_noise, make_input(dtype=torch.uint8))
@@ -4092,14 +4106,28 @@ def test_functional_uint8(self, make_input):
             (F.gaussian_noise, torch.Tensor),
             (F.gaussian_noise_image, tv_tensors.Image),
             (F.gaussian_noise_video, tv_tensors.Video),
+            pytest.param(
+                F._misc._gaussian_noise_image_cvcuda,
+                None,
+                marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CV-CUDA not available"),
+            ),
         ],
     )
     def test_functional_signature(self, kernel, input_type):
+        if kernel is F._misc._gaussian_noise_image_cvcuda:
+            input_type = _import_cvcuda().Tensor
         check_functional_kernel_signature_match(F.gaussian_noise, kernel=kernel, input_type=input_type)
 
     @pytest.mark.parametrize(
         "make_input",
-        [make_image_tensor, make_image, make_video],
+        [
+            make_image_tensor,
+            make_image,
+            make_video,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CV-CUDA not available")
+            ),
+        ],
     )
     def test_transform_float(self, make_input):
         def adapter(_, input, __):
@@ -4117,7 +4145,14 @@ def adapter(_, input, __):
 
     @pytest.mark.parametrize(
         "make_input",
-        [make_image_tensor, make_image, make_video],
+        [
+            make_image_tensor,
+            make_image,
+            make_video,
+            pytest.param(
+                make_image_cvcuda, marks=pytest.mark.skipif(not CVCUDA_AVAILABLE, reason="CV-CUDA not available")
+            ),
+        ],
     )
     def test_transform_uint8(self, make_input):
         def adapter(_, input, __):
diff --git a/torchvision/transforms/v2/_misc.py b/torchvision/transforms/v2/_misc.py
index 305149c87b1..df0a0561962 100644
--- a/torchvision/transforms/v2/_misc.py
+++ b/torchvision/transforms/v2/_misc.py
@@ -9,6 +9,7 @@
 
 from torchvision import transforms as _transforms, tv_tensors
 from torchvision.transforms.v2 import functional as F, Transform
+from torchvision.transforms.v2.functional._utils import _is_cvcuda_tensor
 
 from ._utils import (
     _parse_labels_getter,
@@ -240,6 +241,8 @@ class GaussianNoise(Transform):
             Default is True.
     """
 
+    _transformed_types = Transform._transformed_types + (_is_cvcuda_tensor,)
+
     def __init__(self, mean: float = 0.0, sigma: float = 0.1, clip=True) -> None:
         super().__init__()
         self.mean = mean
diff --git a/torchvision/transforms/v2/functional/_misc.py b/torchvision/transforms/v2/functional/_misc.py
index daf263df046..a3863dadc0b 100644
--- a/torchvision/transforms/v2/functional/_misc.py
+++ b/torchvision/transforms/v2/functional/_misc.py
@@ -1,5 +1,5 @@
 import math
-from typing import Optional
+from typing import Optional, TYPE_CHECKING
 
 import PIL.Image
 import torch
@@ -13,7 +13,12 @@
 
 from ._meta import _convert_bounding_box_format
 
-from ._utils import _get_kernel, _register_kernel_internal, is_pure_tensor
+from ._utils import _get_kernel, _import_cvcuda, _is_cvcuda_available, _register_kernel_internal, is_pure_tensor
+
+CVCUDA_AVAILABLE = _is_cvcuda_available()
+
+if TYPE_CHECKING:
+    import cvcuda  # type: ignore[import-not-found]
 
 
 def normalize(
@@ -231,6 +236,35 @@ def _gaussian_noise_pil(
     raise ValueError("Gaussian Noise is not implemented for PIL images.")
 
 
+def _gaussian_noise_image_cvcuda(
+    image: "cvcuda.Tensor",
+    mean: float = 0.0,
+    sigma: float = 0.1,
+    clip: bool = True,
+) -> "cvcuda.Tensor":
+    cvcuda = _import_cvcuda()
+
+    batch_size = image.shape[0]
+    mu_tensor = cvcuda.as_tensor(torch.full((batch_size,), mean, dtype=torch.float32).cuda(), "N")
+    sigma_tensor = cvcuda.as_tensor(torch.full((batch_size,), sigma, dtype=torch.float32).cuda(), "N")
+
+    # per-channel means each channel gets unique random noise, same behavior as torch.randn_like
+    # produce a seed with torch RNG, if seed is manually set then this will be deterministic
+    # note: clip is not supported in CV-CUDA, so we don't need to clamp the values
+    # by default, clamping is done for floats, and uint8 overflows so is clamped from 0-255 anyways
+    return cvcuda.gaussiannoise(
+        image,
+        mu=mu_tensor,
+        sigma=sigma_tensor,
+        per_channel=True,
+        seed=int(torch.empty((), dtype=torch.int64).random_().item()),
+    )
+
+
+if CVCUDA_AVAILABLE:
+    _register_kernel_internal(gaussian_noise, _import_cvcuda().Tensor)(_gaussian_noise_image_cvcuda)
+
+
 def to_dtype(inpt: torch.Tensor, dtype: torch.dtype = torch.float, scale: bool = False) -> torch.Tensor:
     """See :func:`~torchvision.transforms.v2.ToDtype` for details."""
     if torch.jit.is_scripting():