diff --git a/test/test_transforms_v2.py b/test/test_transforms_v2.py
index 94d90b9e2f6..c87feef11c7 100644
--- a/test/test_transforms_v2.py
+++ b/test/test_transforms_v2.py
@@ -19,6 +19,7 @@
 import torch
 import torchvision.ops
 import torchvision.transforms.v2 as transforms
+from torchvision.transforms.v2 import RandomPosterize
 
 from common_utils import (
     assert_equal,
@@ -6270,4 +6271,20 @@ def test_different_sizes(self, make_input1, make_input2, query):
     @pytest.mark.parametrize("query", [transforms.query_size, transforms.query_chw])
     def test_no_valid_input(self, query):
         with pytest.raises(TypeError, match="No image"):
-            query(["blah"])
+            query(["blah"]
+
+@pytest.mark.parametrize("dtype", [torch.float32, torch.bool, torch.complex64])
+def test_random_posterize_dtype_error(dtype):
+    rp = RandomPosterize(bits=3, p=1.0)
+    tensor = torch.zeros((1, 3, 5, 5), dtype=dtype)
+    with pytest.raises(TypeError) as excinfo:
+        rp(tensor)
+    assert "Number of value bits is only defined for integer dtypes" in str(excinfo.value)
+
+
+def test_random_posterize_uint8_pass():
+    rp = RandomPosterize(bits=4, p=1.0)
+    tensor = torch.randint(0, 255, (1, 3, 5, 5), dtype=torch.uint8)
+    out = rp(tensor)
+    assert isinstance(out, torch.Tensor)
+    assert out.dtype == torch.uint8
diff --git a/torchvision/transforms/v2/_color.py b/torchvision/transforms/v2/_color.py
index bf4ae55d232..198e03398fe 100644
--- a/torchvision/transforms/v2/_color.py
+++ b/torchvision/transforms/v2/_color.py
@@ -3,12 +3,26 @@
 from typing import Any, Optional, Union
 
 import torch
+from torch import Tensor
 from torchvision import transforms as _transforms
 from torchvision.transforms.v2 import functional as F, Transform
 
+
 from ._transform import _RandomApplyTransform
 from ._utils import query_chw
 
+def _ensure_integer_dtype(tensor: Tensor) -> None:
+    """
+    Checks that the tensor's dtype is integer.
+    Throws TypeError for float, complex, bool, etc.
+    """
+    try:
+        torch.iinfo(tensor.dtype)
+    except (ValueError, TypeError):
+        raise TypeError(
+            f"Number of value bits is only defined for integer dtypes, but got {tensor.dtype}"
+        )
+
 
 class Grayscale(Transform):
     """Convert images or videos to grayscale.
@@ -306,9 +320,11 @@ def __init__(self, bits: int, p: float = 0.5) -> None:
         self.bits = bits
 
     def transform(self, inpt: Any, params: dict[str, Any]) -> Any:
+        # Check that the tensor is integer
+        if isinstance(inpt, Tensor):
+            _ensure_integer_dtype(inpt)
         return self._call_kernel(F.posterize, inpt, bits=self.bits)
 
-
 class RandomSolarize(_RandomApplyTransform):
     """Solarize the image or video with a given probability by inverting all pixel
     values above a threshold.