diff --git a/docs/source/onnxruntime/usage_guides/amdgpu.mdx b/docs/source/onnxruntime/usage_guides/amdgpu.mdx
index acd8d732ac3..575f7700ce9 100644
--- a/docs/source/onnxruntime/usage_guides/amdgpu.mdx
+++ b/docs/source/onnxruntime/usage_guides/amdgpu.mdx
@@ -7,11 +7,11 @@ Our testing involved AMD Instinct GPUs, and for specific GPU compatibility, plea
 This guide will show you how to run inference on the `ROCMExecutionProvider` execution provider that ONNX Runtime supports for AMD GPUs.
 
 ## Installation
-The following setup installs the ONNX Runtime support with ROCM Execution Provider with ROCm 5.7. 
+The following setup installs the ONNX Runtime support with ROCM Execution Provider with ROCm 6.0. 
 
 #### 1 ROCm Installation
 
-Refer to the [ROCm installation guide](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html) to install ROCm 5.7.
+Refer to the [ROCm installation guide](https://rocm.docs.amd.com/en/latest/deploy/linux/index.html) to install ROCm 6.0.
 
 #### 2 Installing `onnxruntime-rocm`
 
@@ -26,11 +26,11 @@ docker build -f Dockerfile -t ort/rocm .
 **Local Installation Steps:**
 
 ##### 2.1 PyTorch with ROCm Support
-Optimum ONNX Runtime integration relies on some functionalities of Transformers that require PyTorch. For now, we recommend to use Pytorch compiled against RoCm 5.7, that can be installed following [PyTorch installation guide](https://pytorch.org/get-started/locally/): 
+Optimum ONNX Runtime integration relies on some functionalities of Transformers that require PyTorch. For now, we recommend to use Pytorch compiled against RoCm 6.0, that can be installed following [PyTorch installation guide](https://pytorch.org/get-started/locally/): 
 
 ```bash
-pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/rocm5.7
-# Use 'rocm/pytorch:latest' as the preferred base image when using Docker for PyTorch installation.
+pip3 install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/rocm6.0
+# Use 'rocm/pytorch:rocm6.0.2_ubuntu22.04_py3.10_pytorch_2.1.2' as the preferred base image when using Docker for PyTorch installation.
 ```
 
 ##### 2.2 ONNX Runtime with ROCm Execution Provider
@@ -42,13 +42,13 @@ pip install cmake onnx
 curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
 
 # Install ONNXRuntime from source
-git clone --recursive  https://github.com/ROCmSoftwarePlatform/onnxruntime.git
+git clone --single-branch --branch main --recursive https://github.com/Microsoft/onnxruntime onnxruntime
 cd onnxruntime
-git checkout rocm5.7_internal_testing_eigen-3.4.zip_hash
 
-./build.sh --config Release --build_wheel --update --build --parallel --cmake_extra_defines ONNXRUNTIME_VERSION=$(cat ./VERSION_NUMBER) --use_rocm --rocm_home=/opt/rocm
+./build.sh --config Release --build_wheel --allow_running_as_root --update --build --parallel --cmake_extra_defines CMAKE_HIP_ARCHITECTURES=gfx90a,gfx942 ONNXRUNTIME_VERSION=$(cat ./VERSION_NUMBER) --use_rocm --rocm_home=/opt/rocm
 pip install build/Linux/Release/dist/*
 ```
+Note: The instructions build ORT for `MI210/MI250/MI300` gpus. To support other architectures, please update the `CMAKE_HIP_ARCHITECTURES` in the build command.
 
 <Tip>
 To avoid conflicts between `onnxruntime` and `onnxruntime-rocm`, make sure the package `onnxruntime` is not installed by running `pip uninstall onnxruntime` prior to installing `onnxruntime-rocm`.