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diff --git a/doc/source/ray-core/compiled-graph/overlap.rst b/doc/source/ray-core/compiled-graph/overlap.rst
new file mode 100644
index 0000000000000..fc0e4f6d88649
--- /dev/null
+++ b/doc/source/ray-core/compiled-graph/overlap.rst
@@ -0,0 +1,99 @@
+Overlap communication and computation
+======================================
+
+Compiled Graph currently provides experimental support for GPU communication and computation overlap. When you turn this feature on, it automatically overlaps the GPU communication with computation operations, thereby hiding the communication overhead and improving performance.
+
+To enable this feature, specify ``_overlap_gpu_communication=True`` when calling ``dag.experimental_compile()``.
+
+The following code has GPU communication and computation operations that benefit
+from overlapping.
+
+.. testcode::
+
+    import ray
+    import time
+    import torch
+    from ray.dag import InputNode, MultiOutputNode
+    from ray.experimental.channel.torch_tensor_type import TorchTensorType
+    from ray.air._internal import torch_utils
+
+    @ray.remote(num_cpus=0, num_gpus=1)
+    class TorchTensorWorker:
+        def __init__(self):
+            self.device = torch_utils.get_devices()[0]
+
+        def send(self, shape, dtype, value: int, send_tensor=True):
+            if not send_tensor:
+                return 1
+            return torch.ones(shape, dtype=dtype, device=self.device) * value
+
+        def recv_and_matmul(self, two_d_tensor):
+            """
+            Receive the tensor and do some expensive computation (matmul).
+
+            Args:
+                two_d_tensor: a 2D tensor that has the same size for its dimensions
+            """
+            # Check that tensor got loaded to the correct device.
+            assert two_d_tensor.dim() == 2
+            assert two_d_tensor.size(0) == two_d_tensor.size(1)
+            assert two_d_tensor.device == self.device
+            torch.matmul(two_d_tensor, two_d_tensor)
+            return (two_d_tensor[0][0].item(), two_d_tensor.shape, two_d_tensor.dtype)
+            
+    def test(overlap_gpu_communication):
+        num_senders = 3
+        senders = [TorchTensorWorker.remote() for _ in range(num_senders)]
+        receiver = TorchTensorWorker.remote()
+
+        shape = (10000, 10000)
+        dtype = torch.float16
+
+        with InputNode() as inp:
+            branches = [sender.send.bind(shape, dtype, inp) for sender in senders]
+            branches = [
+                branch.with_type_hint(
+                    TorchTensorType(
+                        transport="nccl", _static_shape=True, _direct_return=True
+                    )
+                )
+                for branch in branches
+            ]
+            branches = [receiver.recv_and_matmul.bind(branch) for branch in branches]
+            dag = MultiOutputNode(branches)
+
+        compiled_dag = dag.experimental_compile(
+            _overlap_gpu_communication=overlap_gpu_communication
+        )
+
+        start = time.monotonic()
+        for i in range(5):
+            ref = compiled_dag.execute(i)
+            result = ray.get(ref)
+            assert result == [(i, shape, dtype)] * num_senders
+        duration = time.monotonic() - start
+        print(f"{overlap_gpu_communication=}, {duration=}")
+
+    if __name__ == "__main__":
+        for overlap_gpu_communication in [False, True]:
+            test(overlap_gpu_communication)
+
+The output of the preceding code includes the following two lines:
+
+.. testoutput::
+
+    overlap_gpu_communication=False, duration=1.0670117866247892
+    overlap_gpu_communication=True, duration=0.9211348341777921
+
+The actual performance numbers may vary on different hardware, but enabling ``_overlap_gpu_communication`` improves latency by about 14% for this example.
+
+To verify that Compiled Graph overlaps the communication and computation operations,
+:ref:`visualize the execution schedule <execution-schedule>` by setting the environment variable
+``RAY_CGRAPH_VISUALIZE_SCHEDULE=1``.
+
+.. image:: ../../images/compiled_graph_schedule_overlap.png
+    :alt: Execution Schedule with GPU Communication Overlap Enabled
+    :align: center
+
+Red nodes denote the operations with different execution orders in the optimized schedule
+compared to the original order, due to ``_overlap_gpu_communication``.
diff --git a/doc/source/ray-core/compiled-graph/ray-compiled-graph.rst b/doc/source/ray-core/compiled-graph/ray-compiled-graph.rst
index e77c33a071192..dcf293882561e 100644
--- a/doc/source/ray-core/compiled-graph/ray-compiled-graph.rst
+++ b/doc/source/ray-core/compiled-graph/ray-compiled-graph.rst
@@ -68,3 +68,4 @@ Learn more details about Ray Compiled Graph from the following links.
     quickstart
     visualization
     profiling
+    overlap
diff --git a/doc/source/ray-core/compiled-graph/visualization.rst b/doc/source/ray-core/compiled-graph/visualization.rst
index d7e7412d122c0..b3577a0a01b29 100644
--- a/doc/source/ray-core/compiled-graph/visualization.rst
+++ b/doc/source/ray-core/compiled-graph/visualization.rst
@@ -50,6 +50,8 @@ The visualization for the preceding code is shown below:
 
 Note that tasks of the same actor are shown in the same color.
 
+.. _execution-schedule:
+
 Execution schedule
 ------------------