diff --git a/docs/en/02-how-to-run/prebuilt_package_windows.md b/docs/en/02-how-to-run/prebuilt_package_windows.md
index 77057caf05..08c5d14122 100644
--- a/docs/en/02-how-to-run/prebuilt_package_windows.md
+++ b/docs/en/02-how-to-run/prebuilt_package_windows.md
@@ -21,7 +21,7 @@
 
 ______________________________________________________________________
 
-This tutorial takes `mmdeploy-1.2.0-windows-amd64.zip` and `mmdeploy-1.2.0-windows-amd64-cuda11.3.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference.
+This tutorial takes `mmdeploy-1.3.0-windows-amd64.zip` and `mmdeploy-1.3.0-windows-amd64-cuda11.8.zip` as examples to show how to use the prebuilt packages. The former support onnxruntime cpu inference, the latter support onnxruntime-gpu and tensorrt inference.
 
 The directory structure of the prebuilt package is as follows, where the `dist` folder is about model converter, and the `sdk` folder is related to model inference.
 
@@ -81,8 +81,8 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API).
 
    ```bash
-   pip install mmdeploy==1.2.0
-   pip install mmdeploy-runtime==1.2.0
+   pip install mmdeploy==1.3.0
+   pip install mmdeploy-runtime==1.3.0
    ```
 
    :point_right: If you have installed it before, please uninstall it first.
@@ -100,7 +100,7 @@ In order to use `ONNX Runtime` backend, you should also do the following steps.
    ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png)
    :exclamation: Restart powershell to make the environment variables setting take effect. You can check whether the settings are in effect by `echo $env:PATH`.
 
-8. Download SDK C/cpp Library mmdeploy-1.2.0-windows-amd64.zip
+8. Download SDK C/cpp Library mmdeploy-1.3.0-windows-amd64.zip
 
 ### TensorRT
 
@@ -109,17 +109,17 @@ In order to use `TensorRT` backend, you should also do the following steps.
 5. Install `mmdeploy` (Model Converter) and `mmdeploy_runtime` (SDK Python API).
 
    ```bash
-   pip install mmdeploy==1.2.0
-   pip install mmdeploy-runtime-gpu==1.2.0
+   pip install mmdeploy==1.3.0
+   pip install mmdeploy-runtime-gpu==1.3.0
    ```
 
    :point_right: If you have installed it before, please uninstall it first.
 
 6. Install TensorRT related package and set environment variables
 
-   - CUDA Toolkit 11.1
-   - TensorRT 8.2.3.0
-   - cuDNN 8.2.1.0
+   - CUDA Toolkit 11.8
+   - TensorRT 8.6.1.6
+   - cuDNN 8.6.0
 
    Add the runtime libraries of TensorRT and cuDNN to the `PATH`. You can refer to the path setting of onnxruntime. Don't forget to install python package of TensorRT.
 
@@ -129,7 +129,7 @@ In order to use `TensorRT` backend, you should also do the following steps.
 
 7. Install pycuda by `pip install pycuda`
 
-8. Download SDK C/cpp Library mmdeploy-1.2.0-windows-amd64-cuda11.3.zip
+8. Download SDK C/cpp Library mmdeploy-1.3.0-windows-amd64-cuda11.8.zip
 
 ## Model Convert
 
@@ -141,7 +141,7 @@ After preparation work, the structure of the current working directory should be
 
 ```
 ..
-|-- mmdeploy-1.2.0-windows-amd64
+|-- mmdeploy-1.3.0-windows-amd64
 |-- mmpretrain
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -189,7 +189,7 @@ After installation of mmdeploy-tensorrt prebuilt package, the structure of the c
 
 ```
 ..
-|-- mmdeploy-1.2.0-windows-amd64-cuda11.3
+|-- mmdeploy-1.3.0-windows-amd64-cuda11.8
 |-- mmpretrain
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -252,8 +252,8 @@ The structure of current working directory：
 
 ```
 .
-|-- mmdeploy-1.2.0-windows-amd64
-|-- mmdeploy-1.2.0-windows-amd64-cuda11.3
+|-- mmdeploy-1.3.0-windows-amd64
+|-- mmdeploy-1.3.0-windows-amd64-cuda11.8
 |-- mmpretrain
 |-- mmdeploy
 |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -324,7 +324,7 @@ The following describes how to use the SDK's C API for inference
 
    It is recommended to use `CMD` here.
 
-   Under `mmdeploy-1.2.0-windows-amd64\\example\\cpp\\build\\Release` directory：
+   Under `mmdeploy-1.3.0-windows-amd64\\example\\cpp\\build\\Release` directory：
 
    ```
    .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG
@@ -344,7 +344,7 @@ The following describes how to use the SDK's C API for inference
 
    It is recommended to use `CMD` here.
 
-   Under `mmdeploy-1.2.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release` directory
+   Under `mmdeploy-1.3.0-windows-amd64-cuda11.8\\example\\cpp\\build\\Release` directory
 
    ```
    .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG
diff --git a/docs/en/get_started.md b/docs/en/get_started.md
index 7bd86b6d31..fd806ae45a 100644
--- a/docs/en/get_started.md
+++ b/docs/en/get_started.md
@@ -230,9 +230,9 @@ result = inference_model(
 You can directly run MMDeploy demo programs in the precompiled package to get inference results.
 
 ```shell
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.0/mmdeploy-1.3.0-linux-x86_64-cuda11.3.tar.gz
-tar xf mmdeploy-1.3.0-linux-x86_64-cuda11.3
-cd mmdeploy-1.3.0-linux-x86_64-cuda11.3
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.0/mmdeploy-1.3.0-linux-x86_64-cuda11.8.tar.gz
+tar xf mmdeploy-1.3.0-linux-x86_64-cuda11.8
+cd mmdeploy-1.3.0-linux-x86_64-cuda11.8
 # run python demo
 python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
 # run C/C++ demo
diff --git a/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md b/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
index 1abf99d0b6..074a0f468b 100644
--- a/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
+++ b/docs/zh_cn/02-how-to-run/prebuilt_package_windows.md
@@ -23,7 +23,7 @@ ______________________________________________________________________
 
 目前，`MMDeploy`在`Windows`平台下提供`cpu`以及`cuda`两种Device的预编译包，其中`cpu`版支持使用onnxruntime cpu进行推理，`cuda`版支持使用onnxruntime-gpu以及tensorrt进行推理，可以从[Releases](https://github.com/open-mmlab/mmdeploy/releases)获取。。
 
-本篇教程以`mmdeploy-1.2.0-windows-amd64.zip`和`mmdeploy-1.2.0-windows-amd64-cuda11.3.zip`为例，展示预编译包的使用方法。
+本篇教程以`mmdeploy-1.3.0-windows-amd64.zip`和`mmdeploy-1.3.0-windows-amd64-cuda11.8.zip`为例，展示预编译包的使用方法。
 
 为了方便使用者快速上手，本教程以分类模型(mmpretrain)为例，展示两种预编译包的使用方法。
 
@@ -89,8 +89,8 @@ ______________________________________________________________________
 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包
 
    ```bash
-   pip install mmdeploy==1.2.0
-   pip install mmdeploy-runtime==1.2.0
+   pip install mmdeploy==1.3.0
+   pip install mmdeploy-runtime==1.3.0
    ```
 
    :point_right: 如果之前安装过，需要先卸载后再安装。
@@ -108,7 +108,7 @@ ______________________________________________________________________
    ![sys-path](https://user-images.githubusercontent.com/16019484/181463801-1d7814a8-b256-46e9-86f2-c08de0bc150b.png)
    :exclamation: 重启powershell让环境变量生效，可以通过 echo $env:PATH 来检查是否设置成功。
 
-8. 下载 SDK C/cpp Library mmdeploy-1.2.0-windows-amd64.zip
+8. 下载 SDK C/cpp Library mmdeploy-1.3.0-windows-amd64.zip
 
 ### TensorRT
 
@@ -117,8 +117,8 @@ ______________________________________________________________________
 5. 安装`mmdeploy`（模型转换）以及`mmdeploy_runtime`（模型推理Python API）的预编译包
 
    ```bash
-   pip install mmdeploy==1.2.0
-   pip install mmdeploy-runtime-gpu==1.2.0
+   pip install mmdeploy==1.3.0
+   pip install mmdeploy-runtime-gpu==1.3.0
    ```
 
    :point_right: 如果之前安装过，需要先卸载后再安装
@@ -137,7 +137,7 @@ ______________________________________________________________________
 
 7. 安装pycuda `pip install pycuda`
 
-8. 下载 SDK C/cpp Library mmdeploy-1.2.0-windows-amd64-cuda11.3.zip
+8. 下载 SDK C/cpp Library mmdeploy-1.3.0-windows-amd64-cuda11.8.zip
 
 ## 模型转换
 
@@ -149,7 +149,7 @@ ______________________________________________________________________
 
 ```
 ..
-|-- mmdeploy-1.2.0-windows-amd64
+|-- mmdeploy-1.3.0-windows-amd64
 |-- mmpretrain
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -197,7 +197,7 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
 
 ```
 ..
-|-- mmdeploy-1.2.0-windows-amd64-cuda11.3
+|-- mmdeploy-1.3.0-windows-amd64-cuda11.8
 |-- mmpretrain
 |-- mmdeploy
 `-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -260,8 +260,8 @@ export2SDK(deploy_cfg, model_cfg, work_dir, pth=model_checkpoint, device=device)
 
 ```
 .
-|-- mmdeploy-1.2.0-windows-amd64
-|-- mmdeploy-1.2.0-windows-amd64-cuda11.3
+|-- mmdeploy-1.3.0-windows-amd64
+|-- mmdeploy-1.3.0-windows-amd64-cuda11.8
 |-- mmpretrain
 |-- mmdeploy
 |-- resnet18_8xb32_in1k_20210831-fbbb1da6.pth
@@ -340,7 +340,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
 
    这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗
 
-   在mmdeploy-1.2.0-windows-amd64\\example\\cpp\\build\\Release目录下：
+   在mmdeploy-1.3.0-windows-amd64\\example\\cpp\\build\\Release目录下：
 
    ```
    .\image_classification.exe cpu C:\workspace\work_dir\onnx\resnet\ C:\workspace\mmpretrain\demo\demo.JPEG
@@ -360,7 +360,7 @@ python .\mmdeploy\demo\python\image_classification.py cpu .\work_dir\onnx\resnet
 
    这里建议使用cmd，这样如果exe运行时如果找不到相关的dll的话会有弹窗
 
-   在mmdeploy-1.2.0-windows-amd64-cuda11.3\\example\\cpp\\build\\Release目录下：
+   在mmdeploy-1.3.0-windows-amd64-cuda11.8\\example\\cpp\\build\\Release目录下：
 
    ```
    .\image_classification.exe cuda C:\workspace\work_dir\trt\resnet C:\workspace\mmpretrain\demo\demo.JPEG
diff --git a/docs/zh_cn/get_started.md b/docs/zh_cn/get_started.md
index 7097d5b434..05ef2b0c27 100644
--- a/docs/zh_cn/get_started.md
+++ b/docs/zh_cn/get_started.md
@@ -113,14 +113,14 @@ mim install "mmcv>=2.0.0rc2"
 
 ```shell
 # 1. 安装 MMDeploy 模型转换工具（含trt/ort自定义算子）
-pip install mmdeploy==1.2.0
+pip install mmdeploy==1.3.0
 
 # 2. 安装 MMDeploy SDK推理工具
 # 根据是否需要GPU推理可任选其一进行下载安装
 # 2.1 支持 onnxruntime 推理
-pip install mmdeploy-runtime==1.2.0
+pip install mmdeploy-runtime==1.3.0
 # 2.2 支持 onnxruntime-gpu tensorrt 推理
-pip install mmdeploy-runtime-gpu==1.2.0
+pip install mmdeploy-runtime-gpu==1.3.0
 
 # 3. 安装推理引擎
 # 3.1 安装推理引擎 TensorRT
@@ -223,10 +223,10 @@ result = inference_model(
 你可以直接运行预编译包中的 demo 程序，输入 SDK Model 和图像，进行推理，并查看推理结果。
 
 ```shell
-wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.2.0/mmdeploy-1.2.0-linux-x86_64-cuda11.3.tar.gz
-tar xf mmdeploy-1.2.0-linux-x86_64-cuda11.3
+wget https://github.com/open-mmlab/mmdeploy/releases/download/v1.3.0/mmdeploy-1.3.0-linux-x86_64-cuda11.8.tar.gz
+tar xf mmdeploy-1.3.0-linux-x86_64-cuda11.8
 
-cd mmdeploy-1.2.0-linux-x86_64-cuda11.3
+cd mmdeploy-1.3.0-linux-x86_64-cuda11.8
 # 运行 python demo
 python example/python/object_detection.py cuda ../mmdeploy_model/faster-rcnn ../mmdetection/demo/demo.jpg
 # 运行 C/C++ demo
diff --git a/mmdeploy/version.py b/mmdeploy/version.py
index a7c77d5ee5..bdf914fb98 100644
--- a/mmdeploy/version.py
+++ b/mmdeploy/version.py
@@ -1,7 +1,7 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 from typing import Tuple
 
-__version__ = '1.2.0'
+__version__ = '1.3.0'
 short_version = __version__
 
 
diff --git a/tools/package_tools/packaging/mmdeploy_runtime/version.py b/tools/package_tools/packaging/mmdeploy_runtime/version.py
index a3c6ea2b01..535755a831 100644
--- a/tools/package_tools/packaging/mmdeploy_runtime/version.py
+++ b/tools/package_tools/packaging/mmdeploy_runtime/version.py
@@ -1,2 +1,2 @@
 # Copyright (c) OpenMMLab. All rights reserved.
-__version__ = '1.2.0'
+__version__ = '1.3.0'