diff --git a/src/awkward/_kernels.py b/src/awkward/_kernels.py
index 28c38cf1db..537aaa750f 100644
--- a/src/awkward/_kernels.py
+++ b/src/awkward/_kernels.py
@@ -12,6 +12,7 @@
 from awkward._nplikes.numpy import Numpy
 from awkward._nplikes.numpy_like import NumpyMetadata
 from awkward._nplikes.typetracer import try_touch_data
+from awkward._nplikes.virtual import materialize_if_virtual
 from awkward._typing import Protocol, TypeAlias
 
 KernelKeyType: TypeAlias = tuple  # Tuple[str, Unpack[Tuple[metadata.dtype, ...]]]
@@ -88,6 +89,8 @@ def _cast(cls, x, t):
     def __call__(self, *args) -> None:
         assert len(args) == len(self._impl.argtypes)
 
+        args = materialize_if_virtual(*args)
+
         return self._impl(
             *(self._cast(x, t) for x, t in zip(args, self._impl.argtypes))
         )
@@ -97,6 +100,8 @@ class JaxKernel(NumpyKernel):
     def __call__(self, *args) -> None:
         assert len(args) == len(self._impl.argtypes)
 
+        args = materialize_if_virtual(*args)
+
         if not any(Jax.is_tracer_type(type(arg)) for arg in args):
             return super().__call__(*args)
 
@@ -138,6 +143,8 @@ def _cast(self, x, type_):
     def __call__(self, *args) -> None:
         import awkward._connect.cuda as ak_cuda
 
+        args = materialize_if_virtual(*args)
+
         cupy = ak_cuda.import_cupy("Awkward Arrays with CUDA")
         maxlength = self.max_length(args)
         grid, blocks = self.calc_grid(maxlength), self.calc_blocks(maxlength)
diff --git a/src/awkward/_nplikes/numpy.py b/src/awkward/_nplikes/numpy.py
index 3043e2c2ed..7157e3a769 100644
--- a/src/awkward/_nplikes/numpy.py
+++ b/src/awkward/_nplikes/numpy.py
@@ -8,6 +8,7 @@
 from awkward._nplikes.dispatch import register_nplike
 from awkward._nplikes.numpy_like import NumpyMetadata
 from awkward._nplikes.placeholder import PlaceholderArray
+from awkward._nplikes.virtual import VirtualArray
 from awkward._typing import TYPE_CHECKING, Final, Literal
 
 if TYPE_CHECKING:
@@ -48,7 +49,8 @@ def is_own_array_type(cls, type_: type) -> bool:
         return issubclass(type_, numpy.ndarray)
 
     def is_c_contiguous(self, x: NDArray | PlaceholderArray) -> bool:
-        if isinstance(x, PlaceholderArray):
+        # TODO: What should this do for virtual arrays?
+        if isinstance(x, (PlaceholderArray, VirtualArray)):
             return True
         else:
             return x.flags["C_CONTIGUOUS"]  # type: ignore[attr-defined]