diff --git a/docs/en/extension/paddle_mobile_inference.md b/docs/en/extension/paddle_mobile_inference.md
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+# Paddle-Lite
+
+[Paddle-Lite](https://github.com/PaddlePaddle/Paddle-Lite) is an open-source deep learning framework designed by PaddlePaddle to make it easy to perform inference on mobile, embeded, and IoT devices.
+Light Weight is reflected in the use of fewer bits to represent the weight and activation of the neural network,
+which can greatly reduce the size of the model,
+solve the problem of limited storage space of the terminal device,
+and the inference performance is overall better than other frame.
+[PaddleClas](https://github.com/PaddlePaddle/PaddleClas) has used Paddle-Lite to evaluate [the performance of the mobile model](../models/Mobile.md).
+For more detail of process, please refer to [Paddle-Lite documentations](https://paddle-lite.readthedocs.io/zh/latest/).
diff --git a/docs/en/extension/paddle_quantization.md b/docs/en/extension/paddle_quantization.md
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+# Model Quantifization
+
+Int8 quantization is one of the key features in [PaddleSlim](https://github.com/PaddlePaddle/PaddleSlim).
+It supports two kinds of training aware, **Dynamic strategy** and **Static strategy**, 
+layer-wise and channel-wise quantization,
+and using PaddleLite to deploy models generated by PaddleSlim.
+
+By using this toolkit, [PaddleClas](https://github.com/PaddlePaddle/PaddleClas) quantized the mobilenet_v3_large_x1_0 model whose accuracy is 78.9% after distilled.
+After quantized, the prediction speed is accelerated from 19.308ms to 14.395ms on SD855. 
+The storage size is reduced from 21M to 10M.
+The top1 recognition accuracy rate is 75.9%.
+For specific training methods, please refer to [PaddleSlim quant aware](https://paddlepaddle.github.io/PaddleSlim/quick_start/quant_aware_tutorial.html)。
+