diff --git a/numba_cuda/numba/cuda/dispatcher.py b/numba_cuda/numba/cuda/dispatcher.py
index f8cf84d56..68f32b971 100644
--- a/numba_cuda/numba/cuda/dispatcher.py
+++ b/numba_cuda/numba/cuda/dispatcher.py
@@ -1626,21 +1626,7 @@ def _compile_for_args(self, *args, **kws):
     def typeof_pyval(self, val):
         # Based on _DispatcherBase.typeof_pyval, but differs from it to support
         # the CUDA Array Interface.
-        try:
-            return typeof(val, Purpose.argument)
-        except ValueError:
-            if (
-                interface := getattr(val, "__cuda_array_interface__")
-            ) is not None:
-                # When typing, we don't need to synchronize on the array's
-                # stream - this is done when the kernel is launched.
-
-                return typeof(
-                    cuda.from_cuda_array_interface(interface, sync=False),
-                    Purpose.argument,
-                )
-            else:
-                raise
+        return typeof(val, Purpose.argument)
 
     def specialize(self, *args):
         """
diff --git a/numba_cuda/numba/cuda/np/numpy_support.py b/numba_cuda/numba/cuda/np/numpy_support.py
index 14d24f90b..b2102fc72 100644
--- a/numba_cuda/numba/cuda/np/numpy_support.py
+++ b/numba_cuda/numba/cuda/np/numpy_support.py
@@ -4,6 +4,7 @@
 import collections
 import ctypes
 import itertools
+import functools
 import operator
 import re
 
@@ -21,11 +22,12 @@
 numpy_version = tuple(map(int, np.__version__.split(".")[:2]))
 
 
+@functools.lru_cache
 def strides_from_shape(
     shape: tuple[int, ...], itemsize: int, *, order: str
 ) -> tuple[int, ...]:
     """Compute strides for a contiguous array with given shape and order."""
-    if len(shape) == 0:
+    if not shape:
         # 0-D arrays have empty strides
         return ()
     limits = slice(1, None) if order == "C" else slice(None, -1)
diff --git a/numba_cuda/numba/cuda/typing/typeof.py b/numba_cuda/numba/cuda/typing/typeof.py
index 2d3e1e826..4e095afbb 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 operator
 
 import numpy as np
 from numpy.random.bit_generator import BitGenerator
@@ -318,17 +319,13 @@ def _typeof_cuda_array_interface(val, c):
     Array Interface. These are typed as regular Array types, with lowering
     handled in numba.cuda.np.arrayobj.
     """
-    # Only handle constants, not arguments (arguments use regular array typing)
-    if c.purpose == Purpose.argument:
-        return None
-
     dtype = numpy_support.from_dtype(np.dtype(val["typestr"]))
     shape = val["shape"]
     ndim = len(shape)
     strides = val.get("strides")
 
     # Determine layout
-    if ndim == 0:
+    if not ndim:
         layout = "C"
     elif strides is None:
         layout = "C"
@@ -340,18 +337,14 @@ def _typeof_cuda_array_interface(val, c):
             c_strides = numpy_support.strides_from_shape(
                 shape, itemsize, order="C"
             )
-            layout = (
-                "C" if all(x == y for x, y in zip(strides, c_strides)) else "A"
-            )
+            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"
             )
-            layout = (
-                "F" if all(x == y for x, y in zip(strides, f_strides)) else "A"
-            )
+            layout = "F" if all(map(operator.eq, strides, f_strides)) else "A"
         else:
             layout = "A"
 
-    readonly = val["data"][1]
+    _, readonly = val["data"]
     return types.Array(dtype, ndim, layout, readonly=readonly)