diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 881ef233e..15fd5ebab 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -14,6 +14,7 @@ repos:
     - id: debug-statements
     - id: end-of-file-fixer
     - id: trailing-whitespace
+      exclude: '.+\.patch'
     - id: mixed-line-ending
       args: ['--fix=lf']
 - repo: https://github.com/astral-sh/ruff-pre-commit
diff --git a/ci/patches/cudf_numba_cuda_compatibility.patch b/ci/patches/cudf_numba_cuda_compatibility.patch
new file mode 100644
index 000000000..689b49c61
--- /dev/null
+++ b/ci/patches/cudf_numba_cuda_compatibility.patch
@@ -0,0 +1,48 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-2-Clause
+diff --git i/python/cudf/cudf/core/buffer/buffer.py w/python/cudf/cudf/core/buffer/buffer.py
+index 71ab509348..e7e6ea9e5f 100644
+--- i/python/cudf/cudf/core/buffer/buffer.py
++++ w/python/cudf/cudf/core/buffer/buffer.py
+@@ -414,7 +414,7 @@ class Buffer(Serializable):
+             "shape": (self.size,),
+             "strides": None,
+             "typestr": "|u1",
+-            "version": 0,
++            "version": 3,
+         }
+ 
+     def serialize(self) -> tuple[dict, list]:
+diff --git i/python/cudf/cudf/core/buffer/spillable_buffer.py w/python/cudf/cudf/core/buffer/spillable_buffer.py
+index 52dcd00e2b..a5bc9a461d 100644
+--- i/python/cudf/cudf/core/buffer/spillable_buffer.py
++++ w/python/cudf/cudf/core/buffer/spillable_buffer.py
+@@ -337,7 +337,7 @@ class SpillableBufferOwner(BufferOwner):
+             "shape": (self.size,),
+             "strides": None,
+             "typestr": "|u1",
+-            "version": 0,
++            "version": 3,
+         }
+ 
+     def memoryview(
+@@ -460,5 +460,5 @@ class SpillableBuffer(ExposureTrackedBuffer):
+             "shape": (self.size,),
+             "strides": None,
+             "typestr": "|u1",
+-            "version": 0,
++            "version": 3,
+         }
+diff --git i/python/cudf/cudf/core/column/column.py w/python/cudf/cudf/core/column/column.py
+index 1562e4810c..a74a9fe23b 100644
+--- i/python/cudf/cudf/core/column/column.py
++++ w/python/cudf/cudf/core/column/column.py
+@@ -1971,7 +1971,7 @@ class ColumnBase(Serializable, BinaryOperand, Reducible):
+             "strides": (self.dtype.itemsize,),
+             "typestr": self.dtype.str,
+             "data": (self.data_ptr, False),
+-            "version": 1,
++            "version": 3,
+         }
+         if self.nullable and self.has_nulls():
+             # Create a simple Python object that exposes the
diff --git a/ci/test_thirdparty_cudf.sh b/ci/test_thirdparty_cudf.sh
index bcdc03f55..4d3d60fd4 100755
--- a/ci/test_thirdparty_cudf.sh
+++ b/ci/test_thirdparty_cudf.sh
@@ -29,6 +29,13 @@ python -m pip install \
 rapids-logger "Shallow clone cuDF repository"
 git clone --single-branch --branch 'release/25.12' https://github.com/rapidsai/cudf.git
 
+# TODO: remove the patch and its application after 26.02 is released
+patchfile="${PWD}/ci/patches/cudf_numba_cuda_compatibility.patch"
+pushd "$(python -c 'import site; print(site.getsitepackages()[0])')"
+# strip 3 slashes to apply from the root of the install
+patch -p3 < "${patchfile}"
+popd
+
 pushd cudf
 
 rapids-logger "Check GPU usage"
diff --git a/numba_cuda/numba/cuda/args.py b/numba_cuda/numba/cuda/args.py
index 9adecfde9..f350613aa 100644
--- a/numba_cuda/numba/cuda/args.py
+++ b/numba_cuda/numba/cuda/args.py
@@ -6,6 +6,9 @@
 memory transfers before & after the kernel call.
 """
 
+import functools
+
+
 from numba.cuda.typing.typeof import typeof, Purpose
 
 
@@ -23,16 +26,16 @@ def to_device(self, retr, stream=0):
             the kernel
         """
 
-    @property
+    @functools.cached_property
     def _numba_type_(self):
         return typeof(self.value, Purpose.argument)
 
 
 class In(ArgHint):
     def to_device(self, retr, stream=0):
-        from .cudadrv.devicearray import auto_device
+        from .cudadrv.devicearray import _to_strided_memory_view
 
-        devary, _ = auto_device(self.value, stream=stream)
+        devary, _ = _to_strided_memory_view(self.value, stream=stream)
         # A dummy writeback functor to keep devary alive until the kernel
         # is called.
         retr.append(lambda: devary)
@@ -40,23 +43,44 @@ def to_device(self, retr, stream=0):
 
 
 class Out(ArgHint):
-    def to_device(self, retr, stream=0):
-        from .cudadrv.devicearray import auto_device
+    copy_input = False
 
-        devary, conv = auto_device(self.value, copy=False, stream=stream)
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import _to_strided_memory_view
+        from .cudadrv.devicearray import _make_strided_memory_view
+        from .cudadrv.driver import driver
+        from .cudadrv import devices
+
+        devary, conv = _to_strided_memory_view(
+            value := self.value, copy=self.__class__.copy_input, stream=stream
+        )
         if conv:
-            retr.append(lambda: devary.copy_to_host(self.value, stream=stream))
+            stream_ptr = getattr(stream, "handle", stream)
+
+            def copy_to_host(devary=devary, value=value, stream_ptr=stream_ptr):
+                hostary = _make_strided_memory_view(
+                    value, stream_ptr=stream_ptr
+                )
+                nbytes = devary.size * devary.dtype.itemsize
+                hostptr = hostary.ptr
+                devptr = devary.ptr
+                if int(stream_ptr):
+                    driver.cuMemcpyDtoHAsync(
+                        hostptr, devptr, nbytes, stream_ptr
+                    )
+                else:
+                    driver.cuMemcpyDtoH(hostptr, devptr, nbytes)
+                    ctx = devices.get_context()
+                    stream = ctx.get_default_stream()
+                    stream.synchronize()
+                return hostary
+
+            retr.append(copy_to_host)
         return devary
 
 
-class InOut(ArgHint):
-    def to_device(self, retr, stream=0):
-        from .cudadrv.devicearray import auto_device
-
-        devary, conv = auto_device(self.value, stream=stream)
-        if conv:
-            retr.append(lambda: devary.copy_to_host(self.value, stream=stream))
-        return devary
+class InOut(Out):
+    copy_input = True
 
 
 def wrap_arg(value, default=InOut):
diff --git a/numba_cuda/numba/cuda/cudadrv/devicearray.py b/numba_cuda/numba/cuda/cudadrv/devicearray.py
index bc6972179..d005f4d5f 100644
--- a/numba_cuda/numba/cuda/cudadrv/devicearray.py
+++ b/numba_cuda/numba/cuda/cudadrv/devicearray.py
@@ -15,6 +15,9 @@
 
 import numpy as np
 
+from cuda.core.utils import StridedMemoryView
+from cuda.core import Buffer
+
 from numba.cuda.cudadrv import devices, dummyarray
 from numba.cuda.cudadrv import driver as _driver
 from numba.cuda import types
@@ -392,13 +395,28 @@ def view(self, dtype):
             gpu_data=self.gpu_data,
         )
 
-    @property
+    @functools.cached_property
     def nbytes(self):
         # Note: not using `alloc_size`.  `alloc_size` reports memory
         # consumption of the allocation, not the size of the array
         # https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.nbytes.html
         return self.dtype.itemsize * self.size
 
+    @functools.cached_property
+    def _strided_memory_view_shim(self):
+        flags = self.flags
+        return _StridedMemoryViewShim(
+            ptr=self.device_ctypes_pointer.value,
+            shape=self.shape,
+            dtype=self.dtype,
+            size=self.size,
+            _layout=_StridedLayoutShim(
+                strides_in_bytes=self.strides,
+                is_contiguous_c=flags["C_CONTIGUOUS"],
+                is_contiguous_f=flags["F_CONTIGUOUS"],
+            ),
+        )
+
 
 class DeviceRecord(DeviceNDArrayBase):
     """
@@ -934,6 +952,137 @@ def auto_device(obj, stream=0, copy=True, user_explicit=False):
         return devobj, True
 
 
+_UNSUPPORTED_DLPACK_TYPES = (
+    np.void,
+    np.datetime64,
+    np.timedelta64,
+    np.bytes_,
+    np.str_,
+)
+
+
+def _make_strided_memory_view(obj, *, stream_ptr) -> StridedMemoryView:
+    if isinstance(obj, _UNSUPPORTED_DLPACK_TYPES) or (
+        isinstance(obj, np.ndarray)
+        and issubclass(obj.dtype.type, _UNSUPPORTED_DLPACK_TYPES)
+    ):
+        return StridedMemoryView.from_array_interface(obj)
+    return StridedMemoryView.from_any_interface(obj, stream_ptr=stream_ptr)
+
+
+class _StridedLayoutShim:
+    __slots__ = ("strides_in_bytes", "is_contiguous_c", "is_contiguous_f")
+
+    def __init__(
+        self,
+        *,
+        strides_in_bytes: tuple[int, ...],
+        is_contiguous_c: bool,
+        is_contiguous_f: bool,
+    ) -> None:
+        self.strides_in_bytes = strides_in_bytes
+        self.is_contiguous_c = is_contiguous_c
+        self.is_contiguous_f = is_contiguous_f
+
+
+class _StridedMemoryViewShim:
+    __slots__ = ("ptr", "shape", "dtype", "size", "_layout")
+
+    def __init__(
+        self,
+        *,
+        ptr: int,
+        shape: tuple[int, ...],
+        dtype: np.dtype,
+        size: int,
+        _layout: _StridedLayoutShim,
+    ) -> None:
+        self.ptr = ptr
+        self.shape = shape
+        self.dtype = dtype
+        self.size = size
+        self._layout = _layout
+
+
+def _to_strided_memory_view(
+    obj, stream=0, copy: bool = True, user_explicit: bool = False
+) -> tuple[StridedMemoryView, bool]:
+    if _driver.is_device_memory(obj):
+        return obj._strided_memory_view_shim, False
+    elif (
+        not isinstance(obj, (np.ndarray, _UNSUPPORTED_DLPACK_TYPES))
+        and hasattr(obj, "__dlpack__")
+        and (
+            (dtype := getattr(obj, "dtype", None)) is None
+            or not issubclass(
+                getattr(dtype, "type", type(None)), _UNSUPPORTED_DLPACK_TYPES
+            )
+        )
+    ):
+        # numpy arrays need to be copied to the device
+        # so we can't view them as SMVs until then
+        #
+        # not sure if this is true in general, since what if a numpy array was
+        # constructed using `np.from_dlpack`?
+        return StridedMemoryView.from_dlpack(
+            obj, stream_ptr=getattr(stream, "handle", stream)
+        ), False
+    elif (desc := getattr(obj, "__cuda_array_interface__", None)) is not None:
+        smv = StridedMemoryView.from_cuda_array_interface(
+            obj, stream_ptr=getattr(stream, "handle", stream)
+        )
+
+        if (
+            external_stream_ptr := desc.get("stream")
+        ) is not None and config.CUDA_ARRAY_INTERFACE_SYNC:
+            ctx = devices.get_context()
+            ext_stream = ctx.create_external_stream(external_stream_ptr)
+            ext_stream.synchronize()
+        return smv, False
+    else:
+        array_obj = np.asanyarray(obj)
+        nbytes = array_obj.nbytes
+        stream_ptr = getattr(stream, "handle", stream)
+
+        ctx = devices.get_context()
+        if not nbytes:
+            # TODO: once cuda-core fixes zero-byte allocation, this branch can go away
+            assert not array_obj.size
+            buf = Buffer.from_handle(
+                ptr=0, size=0, mr=ctx.device._dev.memory_resource
+            )
+        else:
+            # TODO: potentially rebuild EMM around these (cuda-core) APIs instead
+            # of numba-cuda APIs in the future
+            buf = ctx.device._dev.allocate(nbytes, stream=stream)
+
+        hostobj = _make_strided_memory_view(array_obj, stream_ptr=stream_ptr)
+        devobj = StridedMemoryView.from_buffer(
+            buf,
+            shape=hostobj.shape,
+            strides=hostobj.strides,
+            dtype=hostobj.dtype,
+        )
+        if copy:
+            if (
+                config.CUDA_WARN_ON_IMPLICIT_COPY
+                and not config.DISABLE_PERFORMANCE_WARNINGS
+            ):
+                if not user_explicit and (
+                    not isinstance(obj, DeviceNDArray)
+                    and isinstance(obj, np.ndarray)
+                ):
+                    msg = (
+                        "Host array used in CUDA kernel will incur "
+                        "copy overhead to/from device."
+                    )
+                    warn(NumbaPerformanceWarning(msg))
+            _driver.driver.cuMemcpyHtoDAsync(
+                devobj.ptr, hostobj.ptr, nbytes, stream_ptr
+            )
+        return devobj, True
+
+
 def check_array_compatibility(ary1, ary2):
     ary1sq, ary2sq = ary1.squeeze(), ary2.squeeze()
     if ary1.dtype != ary2.dtype:
diff --git a/numba_cuda/numba/cuda/dispatcher.py b/numba_cuda/numba/cuda/dispatcher.py
index acbe13575..c0ee303a4 100644
--- a/numba_cuda/numba/cuda/dispatcher.py
+++ b/numba_cuda/numba/cuda/dispatcher.py
@@ -21,6 +21,7 @@
 from numba.cuda import serialize, utils
 from numba import cuda
 
+from numba.cuda.np import numpy_support
 from numba.cuda.core.compiler_lock import global_compiler_lock
 from numba.cuda.typeconv.rules import default_type_manager
 from numba.cuda.typing.templates import fold_arguments
@@ -41,7 +42,6 @@
 from numba.cuda.core import sigutils, config, entrypoints
 from numba.cuda.flags import Flags
 from numba.cuda.cudadrv import driver, nvvm
-
 from numba.cuda.locks import module_init_lock
 from numba.cuda.core.caching import Cache, CacheImpl, NullCache
 from numba.cuda.descriptor import cuda_target
@@ -560,8 +560,7 @@ def _prepare_args(self, ty, val, stream, retr, kernelargs):
         if isinstance(ty, types.Array):
             devary = wrap_arg(val).to_device(retr, stream)
 
-            meminfo = 0
-            parent = 0
+            meminfo = parent = 0
 
             kernelargs.append(meminfo)
             kernelargs.append(parent)
@@ -571,10 +570,18 @@ def _prepare_args(self, ty, val, stream, retr, kernelargs):
             # however, this saves a noticeable amount of overhead in kernel
             # invocation
             kernelargs.append(devary.size)
-            kernelargs.append(devary.dtype.itemsize)
-            kernelargs.append(devary.device_ctypes_pointer.value)
-            kernelargs.extend(devary.shape)
-            kernelargs.extend(devary.strides)
+            kernelargs.append(itemsize := devary.dtype.itemsize)
+            kernelargs.append(devary.ptr)
+            kernelargs.extend(shape := devary.shape)
+            kernelargs.extend(
+                (layout := devary._layout).strides_in_bytes
+                or numpy_support.strides_from_shape(
+                    shape=shape,
+                    itemsize=itemsize,
+                    c_contiguous=layout.is_contiguous_c,
+                    f_contiguous=layout.is_contiguous_f,
+                )
+            )
 
         elif isinstance(ty, types.CPointer):
             # Pointer arguments should be a pointer-sized integer
@@ -612,7 +619,7 @@ def _prepare_args(self, ty, val, stream, retr, kernelargs):
 
         elif isinstance(ty, types.Record):
             devrec = wrap_arg(val).to_device(retr, stream)
-            kernelargs.append(devrec.device_ctypes_pointer.value)
+            kernelargs.append(devrec.ptr)
 
         elif isinstance(ty, types.BaseTuple):
             assert len(ty) == len(val)
diff --git a/numba_cuda/numba/cuda/np/arrayobj.py b/numba_cuda/numba/cuda/np/arrayobj.py
index 21793a1e1..2922a73f0 100644
--- a/numba_cuda/numba/cuda/np/arrayobj.py
+++ b/numba_cuda/numba/cuda/np/arrayobj.py
@@ -3672,7 +3672,12 @@ def _lower_constant_device_array(context, builder, ty, pyval):
 
     # Calculate strides if not provided (C-contiguous)
     if strides is None:
-        strides = strides_from_shape(shape, itemsize, order="C")
+        strides = strides_from_shape(
+            shape=shape,
+            itemsize=itemsize,
+            c_contiguous=True,
+            f_contiguous=False,
+        )
 
     # Embed device pointer as constant
     llvoidptr = context.get_value_type(types.voidptr)
diff --git a/numba_cuda/numba/cuda/np/numpy_support.py b/numba_cuda/numba/cuda/np/numpy_support.py
index 0a24393b9..91b4d8aeb 100644
--- a/numba_cuda/numba/cuda/np/numpy_support.py
+++ b/numba_cuda/numba/cuda/np/numpy_support.py
@@ -24,14 +24,25 @@
 
 @functools.lru_cache
 def strides_from_shape(
-    shape: tuple[int, ...], itemsize: int, *, order: str
+    *,
+    shape: tuple[int, ...],
+    itemsize: int,
+    c_contiguous: bool,
+    f_contiguous: bool,
 ) -> tuple[int, ...]:
     """Compute strides for a contiguous array with given shape and order."""
     if not shape:
         # 0-D arrays have empty strides
         return ()
-    limits = slice(1, None) if order == "C" else slice(None, -1)
-    transform = reversed if order == "C" else lambda x: x
+
+    # assert that c_contiguous and f_contiguous are not both False
+    assert c_contiguous or f_contiguous, (
+        "either c_contiguous or f_contiguous or both must be True"
+    )
+    limits = (
+        slice(1, None) if c_contiguous and not f_contiguous else slice(None, -1)
+    )
+    transform = reversed if c_contiguous else lambda x: x
     strides = tuple(
         map(
             itemsize.__mul__,
@@ -40,7 +51,7 @@ def strides_from_shape(
             ),
         )
     )
-    if order == "F":
+    if f_contiguous:
         return strides
     return strides[::-1]
 
@@ -110,6 +121,7 @@ def _from_datetime_dtype(dtype):
         raise errors.NumbaNotImplementedError(dtype)
 
 
+@functools.lru_cache
 def from_dtype(dtype):
     """
     Return a Numba Type instance corresponding to the given Numpy *dtype*.
diff --git a/numba_cuda/numba/cuda/simulator/__init__.py b/numba_cuda/numba/cuda/simulator/__init__.py
index 27cb64ed0..fefece6cd 100644
--- a/numba_cuda/numba/cuda/simulator/__init__.py
+++ b/numba_cuda/numba/cuda/simulator/__init__.py
@@ -3,6 +3,7 @@
 
 import sys
 
+from . import args
 from .api import *
 from .vector_types import vector_types
 from .reduction import Reduce
@@ -39,6 +40,7 @@
     from numba.cuda.simulator import cudadrv
     from . import dispatcher
 
+    sys.modules["numba.cuda.args"] = args
     sys.modules["numba.cuda.cudadrv"] = cudadrv
     sys.modules["numba.cuda.cudadrv.devicearray"] = cudadrv.devicearray
     sys.modules["numba.cuda.cudadrv.devices"] = cudadrv.devices
diff --git a/numba_cuda/numba/cuda/simulator/api.py b/numba_cuda/numba/cuda/simulator/api.py
index eb085cb5d..5851471bd 100644
--- a/numba_cuda/numba/cuda/simulator/api.py
+++ b/numba_cuda/numba/cuda/simulator/api.py
@@ -22,7 +22,7 @@
 from .kernel import FakeCUDAKernel
 from numba.cuda.core import config
 from numba.cuda.core.sigutils import is_signature
-from ..args import In, Out, InOut  # noqa: F401
+from .args import In, Out, InOut  # noqa: F401
 
 
 def select_device(dev=0):
diff --git a/numba_cuda/numba/cuda/simulator/args.py b/numba_cuda/numba/cuda/simulator/args.py
new file mode 100644
index 000000000..11ebe1e7c
--- /dev/null
+++ b/numba_cuda/numba/cuda/simulator/args.py
@@ -0,0 +1,66 @@
+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: BSD-2-Clause
+
+"""
+Hints to wrap Kernel arguments to indicate how to manage host-device
+memory transfers before & after the kernel call.
+"""
+
+
+class ArgHint:
+    def __init__(self, value):
+        self.value = value
+
+    def to_device(self, retr, stream=0):
+        """
+        :param stream: a stream to use when copying data
+        :param retr:
+            a list of clean-up work to do after the kernel's been run.
+            Append 0-arg lambdas to it!
+        :return: a value (usually an `DeviceNDArray`) to be passed to
+            the kernel
+        """
+
+
+class In(ArgHint):
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import auto_device
+
+        devary, _ = auto_device(self.value, stream=stream)
+        # A dummy writeback functor to keep devary alive until the kernel
+        # is called.
+        retr.append(lambda: devary)
+        return devary
+
+
+class Out(ArgHint):
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import auto_device
+
+        devary, conv = auto_device(self.value, copy=False, stream=stream)
+        if conv:
+            retr.append(lambda: devary.copy_to_host(self.value, stream=stream))
+        return devary
+
+
+class InOut(ArgHint):
+    def to_device(self, retr, stream=0):
+        from .cudadrv.devicearray import auto_device
+
+        devary, conv = auto_device(self.value, stream=stream)
+        if conv:
+            retr.append(lambda: devary.copy_to_host(self.value, stream=stream))
+        return devary
+
+
+def wrap_arg(value, default=InOut):
+    return value if isinstance(value, ArgHint) else default(value)
+
+
+__all__ = [
+    "In",
+    "Out",
+    "InOut",
+    "ArgHint",
+    "wrap_arg",
+]
diff --git a/numba_cuda/numba/cuda/simulator/kernel.py b/numba_cuda/numba/cuda/simulator/kernel.py
index 5d859b684..4c60971d6 100644
--- a/numba_cuda/numba/cuda/simulator/kernel.py
+++ b/numba_cuda/numba/cuda/simulator/kernel.py
@@ -11,7 +11,7 @@
 from .cudadrv.devicearray import FakeCUDAArray, FakeWithinKernelCUDAArray
 from .kernelapi import Dim3, FakeCUDAModule, swapped_cuda_module
 from ..errors import normalize_kernel_dimensions
-from ..args import ArgHint, InOut
+from .args import ArgHint, InOut
 
 
 """
diff --git a/numba_cuda/numba/cuda/tests/cudapy/test_cuda_array_interface.py b/numba_cuda/numba/cuda/tests/cudapy/test_cuda_array_interface.py
index d9a642144..e246ef9e4 100644
--- a/numba_cuda/numba/cuda/tests/cudapy/test_cuda_array_interface.py
+++ b/numba_cuda/numba/cuda/tests/cudapy/test_cuda_array_interface.py
@@ -10,6 +10,8 @@
 from numba.cuda.tests.support import linux_only, override_config
 from unittest.mock import call, patch
 
+import pytest
+
 
 @skip_on_cudasim("CUDA Array Interface is not supported in the simulator")
 class TestCudaArrayInterface(CUDATestCase):
@@ -80,6 +82,21 @@ def mutate(arr, val):
         np.testing.assert_array_equal(wrapped.copy_to_host(), h_arr + val)
         np.testing.assert_array_equal(d_arr.copy_to_host(), h_arr + val)
 
+    def test_fortran_contiguous(self):
+        cp = pytest.importorskip("cupy")
+
+        @cuda.jit
+        def copy(arr, out):
+            for i in range(arr.shape[0]):
+                for j in range(arr.shape[1]):
+                    out[i, j] = arr[i, j]
+
+        arr = cp.asfortranarray(cp.random.random((10, 10)))
+        out = cp.empty_like(arr)
+        copy[1, 1](arr, out)
+
+        np.testing.assert_array_equal(arr.get(), out.get())
+
     def test_ufunc_arg(self):
         @vectorize(["f8(f8, f8)"], target="cuda")
         def vadd(a, b):
diff --git a/numba_cuda/numba/cuda/tests/cudapy/test_datetime.py b/numba_cuda/numba/cuda/tests/cudapy/test_datetime.py
index 2c513dd0a..ca49435e4 100644
--- a/numba_cuda/numba/cuda/tests/cudapy/test_datetime.py
+++ b/numba_cuda/numba/cuda/tests/cudapy/test_datetime.py
@@ -8,6 +8,8 @@
 from numba.cuda.testing import CUDATestCase, skip_on_cudasim
 import unittest
 
+import pytest
+
 
 class TestCudaDateTime(CUDATestCase):
     def test_basic_datetime_kernel(self):
@@ -58,6 +60,26 @@ def timediff(start, end):
 
         self.assertPreciseEqual(delta, arr2 - arr1)
 
+    @skip_on_cudasim("API unsupported in the simulator")
+    def test_datetime_cupy_inputs(self):
+        cp = pytest.importorskip("cupy")
+        datetime_t = from_dtype(cp.dtype("datetime64[D]"))
+
+        @cuda.jit
+        def assign(out, arr):
+            for i in range(arr.shape[0]):
+                out[i] = arr[i]
+
+        # TODO: cupy doesn't allow passing the datetime64[D] array directly
+        arr = cp.array(
+            np.arange("2005-02", "2006-02", dtype="datetime64[D]").view("int64")
+        ).view("datetime64[D]")
+
+        out = cp.empty_like(arr)
+        assign[1, 1](out, arr)
+
+        self.assertPreciseEqual(arr.get(), out.get())
+
     @skip_on_cudasim("ufunc API unsupported in the simulator")
     def test_gufunc(self):
         datetime_t = from_dtype(np.dtype("datetime64[D]"))
diff --git a/numba_cuda/numba/cuda/typing/typeof.py b/numba_cuda/numba/cuda/typing/typeof.py
index a75ad1a04..fb5985c5b 100644
--- a/numba_cuda/numba/cuda/typing/typeof.py
+++ b/numba_cuda/numba/cuda/typing/typeof.py
@@ -5,6 +5,7 @@
 from functools import singledispatch
 import ctypes
 import enum
+import functools
 import operator
 
 import numpy as np
@@ -311,40 +312,62 @@ def typeof_numpy_polynomial(val, c):
     return types.PolynomialType(coef, domain, window)
 
 
-def _typeof_cuda_array_interface(val, c):
-    """
-    Determine the type of a __cuda_array_interface__ object.
-
-    This handles third-party device arrays that implement the CUDA
-    Array Interface. These are typed as regular Array types, with lowering
-    handled in numba.cuda.np.arrayobj.
-    """
-    dtype = numpy_support.from_dtype(np.dtype(val["typestr"]))
-    shape = val["shape"]
-    ndim = len(shape)
-    strides = val.get("strides")
-
+@functools.lru_cache
+def _typeof_cuda_array_interface_cached(
+    *, dtype, shape, strides, readonly: bool
+):
     # Determine layout
+    ndim = len(shape)
     if not ndim:
         layout = "C"
     elif strides is None:
         layout = "C"
     else:
-        itemsize = np.dtype(val["typestr"]).itemsize
+        itemsize = dtype.bitwidth
         # Quick rejection: C-contiguous has strides[-1] == itemsize,
         # F-contiguous has strides[0] == itemsize. If neither, it's "A".
         if strides[-1] == itemsize:
             c_strides = numpy_support.strides_from_shape(
-                shape, itemsize, order="C"
+                shape=shape,
+                itemsize=itemsize,
+                c_contiguous=True,
+                f_contiguous=False,
             )
             layout = "C" if all(map(operator.eq, strides, c_strides)) else "A"
         elif strides[0] == itemsize:
             f_strides = numpy_support.strides_from_shape(
-                shape, itemsize, order="F"
+                shape=shape,
+                itemsize=itemsize,
+                c_contiguous=False,
+                f_contiguous=True,
             )
             layout = "F" if all(map(operator.eq, strides, f_strides)) else "A"
         else:
             layout = "A"
 
-    _, readonly = val["data"]
     return types.Array(dtype, ndim, layout, readonly=readonly)
+
+
+@functools.lru_cache
+def _numba_dtype_from_str(typestr):
+    return numpy_support.from_dtype(np.dtype(typestr))
+
+
+def _typeof_cuda_array_interface(val, c):
+    """
+    Determine the type of a __cuda_array_interface__ object.
+
+    This handles third-party device arrays that implement the CUDA
+    Array Interface. These are typed as regular Array types, with lowering
+    handled in numba.cuda.np.arrayobj.
+    """
+    dtype = _numba_dtype_from_str(val["typestr"])
+    shape = val["shape"]
+    strides = val.get("strides")
+    _, readonly = val["data"]
+    return _typeof_cuda_array_interface_cached(
+        dtype=dtype,
+        shape=shape,
+        strides=strides,
+        readonly=readonly,
+    )
diff --git a/pixi.lock b/pixi.lock
index 4974ac2d8..490543069 100644
--- a/pixi.lock
+++ b/pixi.lock
@@ -197,7 +197,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-12.0.90-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-impl-2.0.1-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.0.90-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.3.2-py310hba76650_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.5.1-cuda12_py310hfe0b878_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.0.107-hd3aeb46_8.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.0.107-hd3aeb46_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.0.107-h59595ed_8.conda
@@ -303,7 +303,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-12.0.90-h579c4fd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-impl-2.0.1-h8af1aa0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-aarch64-12.0.90-h579c4fd_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.3.2-py310ha183dcc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.5.1-cuda12_py310ha3bd488_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-12.0.107-hac28a21_8.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-dev-12.0.107-hac28a21_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-aarch64-12.0.107-hac28a21_8.conda
@@ -405,7 +405,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cccl-12.0.90-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cccl-impl-2.0.1-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_win-64-12.0.90-h57928b3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.3.2-py310h81eca3c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.5.1-cuda12_py310hd137811_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-12.0.107-h63175ca_8.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-dev-12.0.107-h63175ca_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_win-64-12.0.107-h63175ca_8.conda
@@ -510,7 +510,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-12.0.90-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-impl-2.0.1-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.0.90-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.3.2-py311h2cd87c0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.5.1-cuda12_py311hc26999e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.0.107-hd3aeb46_8.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-dev-12.0.107-hd3aeb46_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-64-12.0.107-h59595ed_8.conda
@@ -616,7 +616,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-12.0.90-h579c4fd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-impl-2.0.1-h8af1aa0_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-aarch64-12.0.90-h579c4fd_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.3.2-py311hdde0560_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.5.1-cuda12_py311hbf63381_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-12.0.107-hac28a21_8.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-dev-12.0.107-hac28a21_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_linux-aarch64-12.0.107-hac28a21_8.conda
@@ -718,7 +718,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cccl-12.0.90-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cccl-impl-2.0.1-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_win-64-12.0.90-h57928b3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.3.2-py311h7c37bad_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.5.1-cuda12_py311hc840b36_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-12.0.107-h63175ca_8.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-dev-12.0.107-h63175ca_8.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cudart-dev_win-64-12.0.107-h63175ca_8.conda
@@ -823,7 +823,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-bindings-12.9.5-py311hff0572f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-12.2.140-ha770c72_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.2.140-ha770c72_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.3.2-py311h2cd87c0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.5.1-cuda12_py311hc26999e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.2.140-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.2.140-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.2.140-hd3aeb46_0.conda
@@ -936,7 +936,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-bindings-12.9.5-py311h1014b6f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-12.2.140-h579c4fd_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-aarch64-12.2.140-h579c4fd_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.3.2-py311hdde0560_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.5.1-cuda12_py311hbf63381_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-aarch64-12.2.140-h579c4fd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-crt-tools-12.2.140-h579c4fd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-12.2.140-hac28a21_0.conda
@@ -1046,7 +1046,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-bindings-12.9.5-py311hfd52643_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cccl-12.2.140-h57928b3_0.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_win-64-12.2.140-h57928b3_0.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.3.2-py311h7c37bad_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.5.1-cuda12_py311hc840b36_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_win-64-12.2.140-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-crt-tools-12.2.140-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-12.2.140-h63175ca_0.conda
@@ -1159,7 +1159,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-bindings-12.9.5-py310ha1f8f56_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-12.8.90-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.3.2-py310hba76650_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.5.1-cuda12_py310hfe0b878_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.93-ha770c72_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.93-ha770c72_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.8.90-h5888daf_1.conda
@@ -1289,7 +1289,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-bindings-12.9.5-py310h2536d29_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-12.8.90-h579c4fd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-aarch64-12.8.90-h579c4fd_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.3.2-py310ha183dcc_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-core-0.5.1-cuda12_py310ha3bd488_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-aarch64-12.8.93-h579c4fd_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-crt-tools-12.8.93-h579c4fd_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cudart-12.8.90-h3ae8b8a_1.conda
@@ -1412,7 +1412,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-bindings-12.9.5-py310h3e8fc49_0.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cccl-12.8.90-h57928b3_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_win-64-12.8.90-h57928b3_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.3.2-py310h81eca3c_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-core-0.5.1-cuda12_py310hd137811_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_win-64-12.8.93-h57928b3_3.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-crt-tools-12.8.93-h57928b3_3.conda
       - conda: https://conda.anaconda.org/conda-forge/win-64/cuda-cudart-12.8.90-he0c23c2_1.conda
@@ -1537,7 +1537,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-bindings-12.9.5-py311hff0572f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cccl-12.8.90-ha770c72_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-64-12.8.90-ha770c72_1.conda
-      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.3.2-py311h2cd87c0_0.conda
+      - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-core-0.5.1-cuda12_py311hc26999e_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-crt-dev_linux-64-12.8.93-ha770c72_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-crt-tools-12.8.93-ha770c72_3.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-64/cuda-cudart-12.8.90-h5888daf_1.conda
@@ -1667,7 +1667,7 @@ environments:
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-bindings-12.9.5-py311h1014b6f_0.conda
       - conda: https://conda.anaconda.org/conda-forge/linux-aarch64/cuda-cccl-12.8.90-h579c4fd_1.conda
       - conda: https://conda.anaconda.org/conda-forge/noarch/cuda-cccl_linux-aarch64-12.8.90-h579c4fd_1.conda
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diff --git a/pixi.toml b/pixi.toml
index a8d16fa87..fa264f1bf 100644
--- a/pixi.toml
+++ b/pixi.toml
@@ -304,7 +304,8 @@ numpy = ">=2.1"
 python = "*"
 packaging = "*"
 numba = ">=0.60"
-cuda-core = ">=0.3,<1"
+cuda-core = ">=0.5.1,<1"
 cuda-bindings = ">=12.9,<14"
 cuda-python = ">=12.9,<14"
 cuda-pathfinder = ">=1.3.1,<2"
+cuda-cudart = "*"
diff --git a/pyproject.toml b/pyproject.toml
index b1268dc7e..00ffce12f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,21 +17,19 @@ requires-python = ">=3.9"
 dependencies = [
     "numba>=0.60.0",
     "cuda-bindings>=12.9.1,<14.0.0",
-    "cuda-core>=0.3.2,<1.0.0",
+    "cuda-core>=0.5.1,<1.0.0",
     "packaging",
 ]
 
 [project.optional-dependencies]
 cu12 = [
     "cuda-bindings>=12.9.1,<13.0.0",
-    "cuda-core>=0.3.0,<1.0.0",
     "cuda-pathfinder>=1.3.1,<2.0.0",
     # install nvcc for libNVVM
     "cuda-toolkit[cudart,nvcc,nvrtc,nvjitlink,cccl]==12.*",
 ]
 cu13 = [
     "cuda-bindings==13.*",
-    "cuda-core>=0.3.2,<1.0.0",
     "cuda-pathfinder>=1.3.1,<2.0.0",
     "cuda-toolkit[cudart,nvvm,nvrtc,nvjitlink,cccl]==13.*",
 ]