diff --git a/intermediate_source/rpc_async_execution.rst b/intermediate_source/rpc_async_execution.rst
index 562d93bbbf8..4c7739104cc 100644
--- a/intermediate_source/rpc_async_execution.rst
+++ b/intermediate_source/rpc_async_execution.rst
@@ -15,8 +15,7 @@ Prerequisites:
 This tutorial demonstrates how to build batch-processing RPC applications with
 the `@rpc.functions.async_execution <https://pytorch.org/docs/master/rpc.html#torch.distributed.rpc.functions.async_execution>`__
 decorator, which helps to speed up training by reducing the number of blocked
-RPC threads and consolidating CUDA operations on the callee. This shares the
-same idea as `Batch Inference with TorchServe <https://pytorch.org/serve/batch_inference_with_ts.html>`__.
+RPC threads and consolidating CUDA operations on the callee.
 
 .. note:: This tutorial requires PyTorch v1.6.0 or above.