diff --git a/python/src/spark_rapids_ml/clustering.py b/python/src/spark_rapids_ml/clustering.py
index c768c62b..67974cdd 100644
--- a/python/src/spark_rapids_ml/clustering.py
+++ b/python/src/spark_rapids_ml/clustering.py
@@ -60,6 +60,7 @@
     _concat_and_free,
     _configure_memory_resource,
     _get_spark_session,
+    _memadvise_cpu,
     get_logger,
     java_uid,
 )
@@ -974,9 +975,7 @@ def _cuml_fit(
                 if cuda_managed_mem_enabled or cuda_system_mem_enabled:
                     # for sam, pin numpy array to host to avoid observed page migration to device during later concatenation
                     if cuda_system_mem_enabled and isinstance(features, np.ndarray):
-                        cp.cuda.runtime.memAdvise(
-                            features.ctypes.data, features.nbytes, 3, -1
-                        )
+                        _memadvise_cpu(features.ctypes.data, features.nbytes)
                     features = cp.array(features)
 
                 inputs.append(features)
diff --git a/python/src/spark_rapids_ml/core.py b/python/src/spark_rapids_ml/core.py
index 088f6823..11e1f282 100644
--- a/python/src/spark_rapids_ml/core.py
+++ b/python/src/spark_rapids_ml/core.py
@@ -83,6 +83,7 @@
     _get_spark_session,
     _is_local,
     _is_standalone_or_localcluster,
+    _memadvise_cpu,
     _SingleNpArrayBatchType,
     _SinglePdDataFrameBatchType,
     dtype_to_pyspark_type,
@@ -922,18 +923,14 @@ def _train_udf(pdf_iter: Iterator[pd.DataFrame]) -> pd.DataFrame:
                         # for sam, pin numpy array to host to avoid observed page migration to device during later concatenation
                         # TODO: revise for non-concatenating algos PCA and LinearRegression which should advise these to device as much as possible
                         if cuda_system_mem_enabled and isinstance(features, np.ndarray):
-                            cp.cuda.runtime.memAdvise(
-                                features.ctypes.data, features.nbytes, 3, -1
-                            )
+                            _memadvise_cpu(features.ctypes.data, features.nbytes)
                         features = cp.array(features)
 
                     # for sam sparse case, pin numpy subarrays to host to avoid observed page migration to device during later concatenation
                     # TODO: revise for non-concatenating algos PCA and LinearRegression
                     if cuda_system_mem_enabled and isinstance(features, csr_matrix):
                         for arr in [features.data, features.indptr, features.indices]:
-                            cp.cuda.runtime.memAdvise(
-                                arr.ctypes.data, arr.nbytes, 3, -1
-                            )
+                            _memadvise_cpu(arr.ctypes.data, arr.nbytes)
 
                     label = pdf[alias.label] if alias.label in pdf.columns else None
                     row_number = (
diff --git a/python/src/spark_rapids_ml/umap.py b/python/src/spark_rapids_ml/umap.py
index 9ab47727..d0e8cb15 100644
--- a/python/src/spark_rapids_ml/umap.py
+++ b/python/src/spark_rapids_ml/umap.py
@@ -96,6 +96,7 @@
     _configure_memory_resource,
     _get_spark_session,
     _is_local,
+    _memadvise_cpu,
     dtype_to_pyspark_type,
     get_logger,
 )
@@ -1200,15 +1201,13 @@ def _train_udf(pdf_iter: Iterable[pd.DataFrame]) -> Iterable[pd.DataFrame]:
                 ) and not use_sparse_array:
                     # for sam, pin numpy array to host to avoid observed page migration to device during later concatenation
                     if cuda_system_mem_enabled and isinstance(features, np.ndarray):
-                        cp.cuda.runtime.memAdvise(
-                            features.ctypes.data, features.nbytes, 3, -1
-                        )
+                        _memadvise_cpu(features.ctypes.data, features.nbytes)
                     features = cp.array(features)
 
                 # for sam sparse case, pin numpy subarrays to host to avoid observed page migration to device during later concatenation
                 if cuda_system_mem_enabled and isinstance(features, csr_matrix):
                     for arr in [features.data, features.indptr, features.indices]:
-                        cp.cuda.runtime.memAdvise(arr.ctypes.data, arr.nbytes, 3, -1)
+                        _memadvise_cpu(arr.ctypes.data, arr.nbytes)
 
                 label = pdf[alias.label] if alias.label in pdf.columns else None
                 row_number = (
diff --git a/python/src/spark_rapids_ml/utils.py b/python/src/spark_rapids_ml/utils.py
index 1d36ea37..f8ac301a 100644
--- a/python/src/spark_rapids_ml/utils.py
+++ b/python/src/spark_rapids_ml/utils.py
@@ -240,6 +240,37 @@ def _configure_memory_resource(
         import spark_rapids_ml.numpy_allocator
 
 
+def _memadvise_cpu(data: Any, nbytes: int) -> None:
+    """
+    Advise data referenced by pointer to stay in cpu memory.
+    For use with SAM to prevent migration of partial arrays staged in host memory to device during
+    gpu concatenation.
+    """
+    import cuda
+    import cupy as cp
+    from packaging.version import parse
+
+    # latest cupy 13.6 has a bug in the cuda13 version of the memadvise api so use low level
+    # python bindings from cuda-python for that case.  Once the patch is released, we can revert to using the cupy api.
+    if parse(cuda.bindings.__version__) < parse("13.0.0"):
+        cp.cuda.runtime.memAdvise(data, nbytes, 3, -1)
+    else:
+        from cuda.bindings.runtime import (
+            cudaMemLocation,
+            cudaMemLocationType,
+            cudaMemoryAdvise,
+        )
+
+        mem_location = cudaMemLocation()
+        mem_location.type = cudaMemLocationType.cudaMemLocationTypeHost
+        cuda.bindings.runtime.cudaMemAdvise(
+            data,
+            nbytes,
+            cudaMemoryAdvise.cudaMemAdviseSetPreferredLocation,
+            mem_location,
+        )
+
+
 def _get_default_params_from_func(
     func: Callable, unsupported_set: List[str] = []
 ) -> Dict[str, Any]: