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@@ -211,6 +211,13 @@ Note: ONNX Runtime is not required to run the MoE layer. It is integrated in sta
 
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+**EOL NOTICE : OpenVINO™ integration with Torch-ORT will no longer be supported as of OpenVINO™ 2023.0 release.**
+
+If you are looking to deploy your PyTorch models on Intel based devices, you have a few options.
+If you prefer the native PyTorch framework APIs, consider using the Intel Extension for PyTorch (IPEX). Another option is to utilize [OpenVINO Model Conversion API](https://docs.openvino.ai/2023.0/openvino_docs_MO_DG_Deep_Learning_Model_Optimizer_DevGuide.html),which enables the automatic importation and conversion of standard PyTorch models during runtime. It is not necessary to convert your PyTorch models offline now.
+
+**END OF EOL NOTICE**
+
 ONNX Runtime for PyTorch supports PyTorch model inference using ONNX Runtime and Intel® OpenVINO™.
 
 It is available via the torch-ort-infer python package. This package enables OpenVINO™ Execution Provider for ONNX Runtime by default for accelerating inference on various Intel® CPUs, Intel® integrated GPUs, and Intel® Movidius™ Vision Processing Units - referred to as VPU.