diff --git a/README.md b/README.md
index 3494ae6..a63332a 100644
--- a/README.md
+++ b/README.md
@@ -354,6 +354,9 @@ If you suffer from lack of GPU memory and you would like to run the model on mor
 
 However, though this method is simple, the efficiency of the native pipeline parallelism is low. We advise you to use vLLM with FastChat and please read the section for deployment.
 
+### x86 Platforms
+When deploy on Core™/Xeon® Scalable Processors or with Arc™ GPU, [OpenVINO™ Toolkit](https://docs.openvino.ai/2023.3/gen_ai_guide.html) is recommended. You can install and run this [example notebook](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/254-llm-chatbot). For related issues, you are welcome to file an issue at [OpenVINO repo](https://github.com/openvinotoolkit/openvino_notebooks/issues). 
+
 ### DashScope
 The most simple way to use Qwen through APIs is DashScope API service through Alibaba Cloud. We give an introduction to the usage. Additionally, we provide a script for you to deploy an OpenAI-style API on your own servers.
 
diff --git a/README_CN.md b/README_CN.md
index fef3db1..4d150d7 100644
--- a/README_CN.md
+++ b/README_CN.md
@@ -347,6 +347,10 @@ model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen-7B-Chat", device_map="cp
 
 尽管这个方法很简单，但它的效率相对较低。我们建议使用vLLM和FastChat并请阅读部署章节。
 
+### x86 平台
+在 酷睿™/至强® 可扩展处理器或 Arc™ GPU 上部署量化模型时，建议使用 [OpenVINO™ Toolkit](https://docs.openvino.ai/2023.3/gen_ai_guide.html) 以充分利用硬件，实现更好的推理性能。您可以安装并运行此[example notebook](https://github.com/openvinotoolkit/openvino_notebooks/tree/main/notebooks/254-llm-chatbot)。相关问题，您可在 [OpenVINO repo](https://github.com/openvinotoolkit/openvino_notebooks/issues)中提交。
+
+
 ### 阿里云灵积（DashScope）API服务
 最简单的使用Qwen模型API服务的方法就是通过DashScope（阿里云灵积API模型服务）。我们提供了简单介绍说明使用方法。同时，我们还提供了自己部署OpenAI格式的API的方法。