diff --git a/examples/image_classifier/densenet_161/README.md b/examples/image_classifier/densenet_161/README.md
index 758c072722..54c805970a 100644
--- a/examples/image_classifier/densenet_161/README.md
+++ b/examples/image_classifier/densenet_161/README.md
@@ -1,3 +1,34 @@
+#### TorchServe inference with torch.compile of densenet161 model
+This example shows how to take eager model of `densenet161`, configure TorchServe to use `torch.compile` and run inference using `torch.compile`
+
+Change directory to the examples directory
+`cd  examples/image_classifier/densenet_161`
+
+`torch.compile` supports a variety of config and the performance you get can vary based on the config. You can find the various options [here](https://pytorch.org/docs/stable/generated/torch.compile.html).
+
+Sample command to start torchserve with torch.compile:
+
+```bash
+wget https://download.pytorch.org/models/densenet161-8d451a50.pth
+mkdir model_store
+torch-model-archiver --model-name densenet161 --version 1.0 --model-file model.py --serialized-file densenet161-8d451a50.pth --export-path model_store --extra-files ../../image_classifier/index_to_name.json --handler image_classifier --config-file model-config.yaml -f
+torchserve --start --ncs --model-store model_store --models densenet161.mar
+curl http://127.0.0.1:8080/predictions/densenet161 -T ../../image_classifier/kitten.jpg
+```
+
+produces the output
+
+```
+{
+  "tabby": 0.4664836823940277,
+  "tiger_cat": 0.4645617604255676,
+  "Egyptian_cat": 0.06619937717914581,
+  "lynx": 0.0012969186063855886,
+  "plastic_bag": 0.00022856894065625966
+}
+```
+
+
 #### Sample commands to create a densenet eager mode model archive, register it on TorchServe and run image prediction
 
 Run the commands given in following steps from the parent directory of the root of the repository. For example, if you cloned the repository into /home/my_path/serve, run the steps from /home/my_path/serve
@@ -35,8 +66,8 @@ model.eval()
 example_input = torch.rand(1, 3, 224, 224)
 traced_script_module = torch.jit.trace(model, example_input)
 traced_script_module.save("densenet161.pt")
-``` 
- 
+```
+
 * Use following commands to register Densenet161 torchscript model on TorchServe and run image prediction
 
 ```bash
diff --git a/examples/image_classifier/densenet_161/model-config.yaml b/examples/image_classifier/densenet_161/model-config.yaml
new file mode 100644
index 0000000000..b362781ef4
--- /dev/null
+++ b/examples/image_classifier/densenet_161/model-config.yaml
@@ -0,0 +1,5 @@
+pt2:
+  compile:
+    enable: True
+    backend: inductor
+    mode: reduce-overhead