diff --git a/demo/rmm_plugin/README.rst b/demo/rmm_plugin/README.rst
index 4742507d2..28b816eb2 100644
--- a/demo/rmm_plugin/README.rst
+++ b/demo/rmm_plugin/README.rst
@@ -1,5 +1,5 @@
-Using XGBoost with RAPIDS Memory Manager (RMM) plugin (EXPERIMENTAL)
-====================================================================
+Using XGBoost with RAPIDS Memory Manager (RMM) plugin
+=====================================================
 
 `RAPIDS Memory Manager (RMM) <https://github.com/rapidsai/rmm>`__ library provides a
 collection of efficient memory allocators for NVIDIA GPUs. It is now possible to use
@@ -47,5 +47,15 @@ the global configuration ``use_rmm``:
   with xgb.config_context(use_rmm=True):
     clf = xgb.XGBClassifier(tree_method="hist", device="cuda")
 
-Depending on the choice of memory pool size or type of allocator, this may have negative
-performance impact.
+Depending on the choice of memory pool size and the type of the allocator, this can add
+more consistency to memory usage but with slightly degraded performance impact.
+
+*******************************
+No Device Ordinal for Multi-GPU
+*******************************
+
+Since with RMM the memory pool is pre-allocated on a specific device, changing the CUDA
+device ordinal in XGBoost can result in memory error ``cudaErrorIllegalAddress``. Use the
+``CUDA_VISIBLE_DEVICES`` environment variable instead of the ``device="cuda:1"`` parameter
+for selecting device. For distributed training, the distributed computing frameworks like
+``dask-cuda`` are responsible for device management.
\ No newline at end of file
diff --git a/doc/parameter.rst b/doc/parameter.rst
index 00f0eaea6..a77655922 100644
--- a/doc/parameter.rst
+++ b/doc/parameter.rst
@@ -25,7 +25,11 @@ Global Configuration
 The following parameters can be set in the global scope, using :py:func:`xgboost.config_context()` (Python) or ``xgb.set.config()`` (R).
 
 * ``verbosity``: Verbosity of printing messages. Valid values of 0 (silent), 1 (warning), 2 (info), and 3 (debug).
-* ``use_rmm``: Whether to use RAPIDS Memory Manager (RMM) to allocate GPU memory. This option is only applicable when XGBoost is built (compiled) with the RMM plugin enabled. Valid values are ``true`` and ``false``.
+
+* ``use_rmm``: Whether to use RAPIDS Memory Manager (RMM) to allocate cache GPU
+  memory. The primary memory is always allocated on the RMM pool when XGBoost is built
+  (compiled) with the RMM plugin enabled. Valid values are ``true`` and ``false``. See
+  :doc:`/python/rmm-examples/index` for details.
 
 ******************
 General Parameters