diff --git a/java/src/main/native/src/RmmJni.cpp b/java/src/main/native/src/RmmJni.cpp
index 0f424761bfe..8c733018fa7 100644
--- a/java/src/main/native/src/RmmJni.cpp
+++ b/java/src/main/native/src/RmmJni.cpp
@@ -778,17 +778,11 @@ JNIEXPORT jlong JNICALL Java_ai_rapids_cudf_Rmm_newCudaAsyncMemoryResource(
   try {
     cudf::jni::auto_set_device(env);
 
-    // When we are using fabric, we need to set the memory access to be
-    // read_write, in order for peer GPUs to have access to this memory.
-    // Otherwise, choose default parameters (optional set to nullopt).
-    auto [handle_type, prot_flag] =
-      fabric
-        ? std::pair{std::optional{
-                      rmm::mr::cuda_async_memory_resource::allocation_handle_type::fabric},
-                    std::optional{rmm::mr::cuda_async_memory_resource::access_flags::read_write}}
-        : std::pair{std::nullopt, std::nullopt};
-
-    auto ret = new rmm::mr::cuda_async_memory_resource(init, release, handle_type, prot_flag);
+    auto handle_type =
+      fabric ? std::optional{rmm::mr::cuda_async_memory_resource::allocation_handle_type::fabric}
+             : std::nullopt;
+
+    auto ret = new rmm::mr::cuda_async_memory_resource(init, release, handle_type);
 
     return reinterpret_cast<jlong>(ret);
   }