diff --git a/examples/pytorch/cpu_offloading/cpu_offload.py b/examples/pytorch/cpu_offloading/cpu_offload.py
new file mode 100644
index 0000000000..8a8815dd9c
--- /dev/null
+++ b/examples/pytorch/cpu_offloading/cpu_offload.py
@@ -0,0 +1,32 @@
+import torch
+import transformer_engine as te
+
+from transformer_engine.pytorch.cpu_offload import get_cpu_offload_context
+
+# Initialize a CPU offload context to enable activation offloading and set number of layers
+# to be offloaded to 1
+context, sync_func = get_cpu_offload_context(True, 1, True, False)
+
+
+# Define a 2 Linear layer model
+layer = []
+for i in range(2):
+    layer.append(te.pytorch.Linear(1024, 1024, bias=False, device="cuda"))
+
+# Create dummy inputs on GPU
+input_state = torch.rand(1024, 1024).cuda()
+
+# Wrap the forward prop under the context
+with context:
+    hidden = layer[0](input_state)
+
+# Use synchronize function to sync across layers
+hidden = sync_func(hidden)
+
+with context:
+    output = layer[1](hidden)
+
+output = sync_func(output)
+
+# Trigger backward
+output.sum().backward()