diff --git a/python/cuda_cccl/cuda/cccl/cooperative/experimental/_types.py b/python/cuda_cccl/cuda/cccl/cooperative/experimental/_types.py
index 76d031d368a..9c372c04a97 100644
--- a/python/cuda_cccl/cuda/cccl/cooperative/experimental/_types.py
+++ b/python/cuda_cccl/cuda/cccl/cooperative/experimental/_types.py
@@ -3,7 +3,6 @@
 # SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
 
 import re
-from functools import cached_property
 from io import StringIO
 from textwrap import dedent
 from types import FunctionType as PyFunctionType
@@ -15,6 +14,8 @@
 from numba.core import cgutils
 from numba.core.extending import intrinsic, overload
 from numba.core.typing import signature
+from numba.cuda import LTOIR
+from numba.cuda.cudadrv import driver as cuda_driver
 
 import cuda.cccl.cooperative.experimental._nvrtc as nvrtc
 from cuda.cccl.cooperative.experimental._common import find_unsigned
@@ -553,6 +554,8 @@ def __init__(
         self.parameters = parameters
         self.type_definitions = type_definitions
         self.fake_return = fake_return
+        self._temp_storage_bytes = None
+        self._temp_storage_alignment = None
 
     def __repr__(self) -> str:
         return f"{self.struct_name}::{self.method_name}{self.template_parameters}: {self.parameters}"
@@ -604,43 +607,22 @@ def specialize(self, template_arguments):
             fake_return=self.fake_return,
         )
 
-    @cached_property
-    def _temp_storage_bytes_and_alignment(self):
-        algorithm_name = self.struct_name
-        includes = self.includes or []
-        type_definitions = self.type_definitions or []
-
-        buf = StringIO()
-        w = buf.write
-
-        w("#include <cuda/std/cstdint>\n")
-        for include in includes:
-            w(f"#include <{include}>\n")
-        for type_definition in type_definitions:
-            w(f"{type_definition.code}\n")
-
-        w(f"using algorithm_t = cub::{algorithm_name};\n")
-        w("using temp_storage_t = typename algorithm_t::TempStorage;\n")
-        prefix = "__device__ constexpr unsigned temp_storage_"
-        w(f"{prefix}bytes = sizeof(temp_storage_t);\n")
-        w(f"{prefix}alignment = alignof(temp_storage_t);\n")
-
-        src = buf.getvalue()
-        device = cuda.get_current_device()
-        cc_major, cc_minor = device.compute_capability
-        cc = cc_major * 10 + cc_minor
-        _, ptx = nvrtc.compile(cpp=src, cc=cc, rdc=True, code="ptx")
-        temp_storage_bytes = find_unsigned("temp_storage_bytes", ptx)
-        temp_storage_alignment = find_unsigned("temp_storage_alignment", ptx)
-        return (temp_storage_bytes, temp_storage_alignment)
-
     @property
     def temp_storage_bytes(self):
-        return self._temp_storage_bytes_and_alignment[0]
+        if self._temp_storage_bytes is None:
+            raise RuntimeError(
+                "Temporary storage bytes not computed yet.  Call get_lto_ir() first."
+            )
+        return self._temp_storage_bytes
 
     @property
     def temp_storage_alignment(self):
-        return self._temp_storage_bytes_and_alignment[1]
+        if self._temp_storage_alignment is None:
+            raise RuntimeError(
+                "Temporary storage alignment not computed yet.  "
+                "Call get_lto_ir() first."
+            )
+        return self._temp_storage_alignment
 
     def get_lto_ir(self, threads=None):
         lto_irs = []
@@ -680,6 +662,9 @@ def get_lto_ir(self, threads=None):
 
         w(f"using algorithm_t = cub::{algorithm_name};\n")
         w("using temp_storage_t = typename algorithm_t::TempStorage;\n")
+        prefix = "__device__ constexpr unsigned temp_storage_"
+        w(f"{prefix}bytes = sizeof(temp_storage_t);\n")
+        w(f"{prefix}alignment = alignof(temp_storage_t);\n")
 
         src = buf.getvalue()
 
@@ -775,8 +760,24 @@ def get_lto_ir(self, threads=None):
         cc = cc_major * 10 + cc_minor
         # N.B. Uncomment this to immediately print generated source to stdout.
         # print(src)
-        _, lto_fn = nvrtc.compile(cpp=src, cc=cc, rdc=True, code="lto")
-        lto_irs.append(lto_fn)
+        _, blob = nvrtc.compile(cpp=src, cc=cc, rdc=True, code="lto")
+        lto_irs.append(blob)
+
+        # Convert the LTO into PTX in order to extract the size and alignment
+        # variables.
+        obj = LTOIR(name=self.c_name, data=blob)
+        linker = cuda_driver._Linker.new(
+            cc=device.compute_capability,
+            additional_flags=["-ptx"],
+            lto=obj,
+        )
+        ltoir_bytes = obj.data
+        linker.add_ltoir(ltoir_bytes)
+        ptx = linker.get_linked_ptx()
+        ptx = ptx.decode("utf-8")
+        self._temp_storage_bytes = find_unsigned("temp_storage_bytes", ptx)
+        self._temp_storage_alignment = find_unsigned("temp_storage_alignment", ptx)
+
         return lto_irs
 
     def codegen(self, func_to_overload):