diff --git a/doc/source/configure.rst b/doc/source/configure.rst
index 83a700a9e..b1cadc2fa 100644
--- a/doc/source/configure.rst
+++ b/doc/source/configure.rst
@@ -60,7 +60,9 @@ If using the command line, connect to the Ray cluster as follow:
     override this by explicitly setting ``OMP_NUM_THREADS``. ``OMP_NUM_THREADS`` is commonly
     used in numpy, PyTorch, and Tensorflow to perform multit-threaded linear algebra.
     In multi-worker setting, we want one thread per worker instead of many threads
-    per worker to avoid contention.
+    per worker to avoid contention. Some other libraries may have their own way to configure
+    parallelism. For example, if you're using OpenCV, you should manually set the number of
+    threads using cv2.setNumThreads(num_threads) (set to 0 to disable multi-threading).
 
 
 .. _temp-dir-log-files:
diff --git a/doc/source/serve/advanced.rst b/doc/source/serve/advanced.rst
index 26003559d..cdca01c62 100644
--- a/doc/source/serve/advanced.rst
+++ b/doc/source/serve/advanced.rst
@@ -81,6 +81,12 @@ If you *do* want to enable this parallelism in your Serve backend, just set OMP_
 
   client.create_backend("parallel_backend", MyBackend, 12)
 
+
+.. note::
+  Some other libraries may not respect ``OMP_NUM_THREADS`` and have their own way to configure parallelism.
+  For example, if you're using OpenCV, you'll need to manually set the number of threads using ``cv2.setNumThreads(num_threads)`` (set to 0 to disable multi-threading).
+  You can check the configuration using ``cv2.getNumThreads()`` and ``cv2.getNumberOfCPUs()``.
+
 .. _serve-batching:
 
 Batching to improve performance