diff --git a/cuda_core/cuda/core/experimental/_device.py b/cuda_core/cuda/core/experimental/_device.py
index 0c03c789..a5cd4bc7 100644
--- a/cuda_core/cuda/core/experimental/_device.py
+++ b/cuda_core/cuda/core/experimental/_device.py
@@ -7,7 +7,7 @@
 
 from cuda import cuda, cudart
 from cuda.core.experimental._context import Context, ContextOptions
-from cuda.core.experimental._memory import Buffer, MemoryResource, _DefaultAsyncMempool
+from cuda.core.experimental._memory import Buffer, MemoryResource, _AsyncMemoryResource, _DefaultAsyncMempool
 from cuda.core.experimental._stream import Stream, StreamOptions, default_stream
 from cuda.core.experimental._utils import ComputeCapability, CUDAError, handle_return, precondition
 
@@ -62,7 +62,13 @@ def __new__(cls, device_id=None):
                 for dev_id in range(total):
                     dev = super().__new__(cls)
                     dev._id = dev_id
-                    dev._mr = _DefaultAsyncMempool(dev_id)
+                    # If the device is in TCC mode, or does not support memory pools for some other reason,
+                    # use the AsyncMemoryResource which does not use memory pools.
+                    if (handle_return(cudart.cudaGetDeviceProperties(dev_id))).memoryPoolsSupported == 0:
+                        dev._mr = _AsyncMemoryResource(dev_id)
+                    else:
+                        dev._mr = _DefaultAsyncMempool(dev_id)
+
                     dev._has_inited = False
                     _tls.devices.append(dev)
 
@@ -70,7 +76,7 @@ def __new__(cls, device_id=None):
 
     def _check_context_initialized(self, *args, **kwargs):
         if not self._has_inited:
-            raise CUDAError("the device is not yet initialized, perhaps you forgot to call .set_current() first?")
+            raise CUDAError("the device is not yet initialized, " "perhaps you forgot to call .set_current() first?")
 
     @property
     def device_id(self) -> int:
diff --git a/cuda_core/cuda/core/experimental/_memory.py b/cuda_core/cuda/core/experimental/_memory.py
index 415b5151..50f8a260 100644
--- a/cuda_core/cuda/core/experimental/_memory.py
+++ b/cuda_core/cuda/core/experimental/_memory.py
@@ -42,7 +42,11 @@ class Buffer:
     """
 
     # TODO: handle ownership? (_mr could be None)
-    __slots__ = ("_ptr", "_size", "_mr")
+    __slots__ = (
+        "_ptr",
+        "_size",
+        "_mr",
+    )
 
     def __init__(self, ptr, size, mr: MemoryResource = None):
         self._ptr = ptr
@@ -286,3 +290,34 @@ def is_host_accessible(self) -> bool:
     @property
     def device_id(self) -> int:
         raise RuntimeError("the pinned memory resource is not bound to any GPU")
+
+
+class _AsyncMemoryResource(MemoryResource):
+    __slots__ = ("_dev_id",)
+
+    def __init__(self, dev_id):
+        self._handle = None
+        self._dev_id = dev_id
+
+    def allocate(self, size, stream=None) -> Buffer:
+        if stream is None:
+            stream = default_stream()
+        ptr = handle_return(cuda.cuMemAllocAsync(size, stream._handle))
+        return Buffer(ptr, size, self)
+
+    def deallocate(self, ptr, size, stream=None):
+        if stream is None:
+            stream = default_stream()
+        handle_return(cuda.cuMemFreeAsync(ptr, stream._handle))
+
+    @property
+    def is_device_accessible(self) -> bool:
+        return True
+
+    @property
+    def is_host_accessible(self) -> bool:
+        return False
+
+    @property
+    def device_id(self) -> int:
+        return self._dev_id