Its a bit tricky, but I got it working for me with my RX 7900XTX on Windows 11. They said native Windows support for ROCm is coming, but my guess is that it will be another year or two until it will be released, so currently only WSL with ubuntu on windows.

The problem is the documentation has gotten better but for someone who doesn´t want to spend hours on it, here is my stuff which works.

So the documentation sites I got all of it from are those:

rocm.docs.amd.com/en/latest/

rocm.docs.amd.com/projects/radeon/en/latest/index.html

rocm.docs.amd.com/projects/radeon/en/latest/docs/install/wsl/howto_wsl.html

rocm.docs.amd.com/projects/radeon/en/latest/docs/install/wsl/install-radeon.html

rocm.docs.amd.com/projects/radeon/en/latest/docs/install/wsl/install-pytorch.html

But as a short instruction here is the installation instructions from start to finish.

First install WSL and the currently only supported distribution of linux for WSL with ROCm which is 22.04 using cmd in admin mode, you will need to setup a username and password for the distribution once its installed.

wsl --install -d Ubuntu-22.04

then after install do this inside the distribution in which you can get to in cmd using command:

wsl

then to just update the install of ubuntu to the newest version for its components do those two commands:

sudo apt-get update

sudo apt-get upgrade

then to install the drivers and install rocm do this:

sudo apt update

wget https://repo.radeon.com/amdgpu-install/6.1.3/ubuntu/jammy/amdgpu-install_6.1.60103-1_all.deb

sudo apt install ./amdgpu-install_6.1.60103-1_all.deb

amdgpu-install -y --usecase=wsl,rocm --no-dkms

And then you have the base of rocm and the driver installed, then you need to install python and pytorch. Notice the only supported version is Python 3.10 with pytorch 2.1.2 as of my knowledge.

To install python with pytorch follow those instructions, as of my last use it will automatically install python 3.10:

sudo apt install python3-pip -y

pip3 install --upgrade pip wheel

wget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/torch-2.1.2%2Brocm6.1.3-cp310-cp310-linux_x86_64.whl

wget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/torchvision-0.16.1%2Brocm6.1.3-cp310-cp310-linux_x86_64.whl

wget https://repo.radeon.com/rocm/manylinux/rocm-rel-6.1.3/pytorch_triton_rocm-2.1.0%2Brocm6.1.3.4d510c3a44-cp310-cp310-linux_x86_64.whl

pip3 uninstall torch torchvision pytorch-triton-rocm numpy

pip3 install torch-2.1.2+rocm6.1.3-cp310-cp310-linux_x86_64.whl torchvision-0.16.1+rocm6.1.3-cp310-cp310-linux_x86_64.whl pytorch_triton_rocm-2.1.0+rocm6.1.3.4d510c3a44-cp310-cp310-linux_x86_64.whl numpy==1.26.4

The next is just updating to the WSL compatible runtime lib:

location=`pip show torch | grep Location | awk -F ": " '{print $2}'`

cd ${location}/torch/lib/

rm libhsa-runtime64.so*

cp /opt/rocm/lib/libhsa-runtime64.so.1.2 libhsa-runtime64.so

Then everything should be setup and running. To check if it worked use those commands in WSL:

python3 -c 'import torch; print(torch.cuda.is_available())'

python3 -c "import torch; print(f'device name [0]:', torch.cuda.get_device_name(0))"

python3 -m torch.utils.collect_env

Hope those instructions help other lost souls who are trying to get ROCm working and escape the Nvidia monopoly but unfortunately I have also an Nvidia RTX 2080ti and my RX 7900XTX can do larger batches in training, but is like a third slower than the older Nvidia card, but in Inference I see similar speeds.
Maybe someone has some optimization ideas to get it up to speed?

The support matrix for the supported GPUs and Ubuntu versions are here:

https://rocm.docs.amd.com/projects/radeon/en/latest/docs/compatibility/wsl/wsl_compatibility.html

If anything went wrong I can test it again. Hope also the links to the specific documentation sites are helpful if anything slightly changes from my installation instructions.

Small endnote, it took me months and hours of frustration to get this instructions working for myself, hope I spared you from that with this. And I noticed that if I only used another version of pytorch than the one above it will not work, even if they say pytorch in the nightly build with version 2.5.0 is supported, believe me I tried and it did not work.

After extensive testing, I've successfully installed ROCm 7.0 with PyTorch 2.8.0 for AMD RX 6900 XT (gfx1030 architecture) on Ubuntu 24.04.2. The setup runs ComfyUI's Wan2.2 image-to-video workflow flawlessly at 640×640 resolution with 81 frames. Here's my verified installation procedure:

🚀 Prerequisites

• Fresh Ubuntu 24.04.2 LTS installation

• AMD RX 6000 series GPU (gfx1030 architecture)

• Internet connection for package downloads

📋 Installation Steps

1. System Preparation

sudo apt install environment-modules

2. User Group Configuration

Why: Required for GPU access permissions

# Check current groups
groups

# Add current user to required groups
sudo usermod -a -G video,render $LOGNAME

# Optional: Add future users automatically
echo 'ADD_EXTRA_GROUPS=1' | sudo tee -a /etc/adduser.conf
echo 'EXTRA_GROUPS=video' | sudo tee -a /etc/adduser.conf
echo 'EXTRA_GROUPS=render' | sudo tee -a /etc/adduser.conf

3. Install ROCm 7.0 Packages

sudo apt update
wget https://repo.radeon.com/amdgpu/7.0/ubuntu/pool/main/a/amdgpu-insecure-instinct-udev-rules/amdgpu-insecure-instinct-udev-rules_30.10.0.0-2204008.24.04_all.deb
sudo apt install ./amdgpu-insecure-instinct-udev-rules_30.10.0.0-2204008.24.04_all.deb

wget https://repo.radeon.com/amdgpu-install/7.0/ubuntu/noble/amdgpu-install_7.0.70000-1_all.deb
sudo apt install ./amdgpu-install_7.0.70000-1_all.deb
sudo apt update
sudo apt install python3-setuptools python3-wheel
sudo apt install rocm

4. Kernel Modules and Drivers

sudo apt install "linux-headers-$(uname -r)" "linux-modules-extra-$(uname -r)"
sudo apt install amdgpu-dkms

5. Environment Configuration

# Configure ROCm shared objects
sudo tee --append /etc/ld.so.conf.d/rocm.conf <<EOF
/opt/rocm/lib
/opt/rocm/lib64
EOF
sudo ldconfig

# Set library path (crucial for multi-version installs)
export LD_LIBRARY_PATH=/opt/rocm-7.0.0/lib

# Install OpenCL runtime
sudo apt install rocm-opencl-runtime

6. Verification

# Check ROCm installation
rocminfo
clinfo

7. Python Environment Setup

sudo apt install python3.12-venv
python3 -m venv comfyui-pytorch
source ./comfyui-pytorch/bin/activate

8. PyTorch Installation with ROCm 7.0 Support

pip install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.0/pytorch_triton_rocm-3.4.0%2Brocm7.0.0.gitf9e5bf54-cp312-cp312
pip install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.0/torch-2.8.0%2Brocm7.0.0.lw.git64359f59-cp312-cp312-linux_x86_64.whl
pip install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.0/torchvision-0.24.0%2Brocm7.0.0.gitf52c4f1a-cp312-cp312-linux_x86_64.whl
pip install https://repo.radeon.com/rocm/manylinux/rocm-rel-7.0/torchaudio-2.8.0%2Brocm7.0.0.git6e1c7fe9-cp312-cp312-linux_x86_64.whl

9. ComfyUI Installation

git clone https://github.com/comfyanonymous/ComfyUI.git
cd ComfyUI
pip install -r requirements.txt

✅ Verified Package Versions

ROCm Components:

• ROCm 7.0.0

• amdgpu-dkms: latest

• rocm-opencl-runtime: 7.0.0

PyTorch Stack:

• pytorch-triton-rocm: 3.4.0+rocm7.0.0.gitf9e5bf54

• torch: 2.8.0+rocm7.0.0.lw.git64359f59

• torchvision: 0.24.0+rocm7.0.0.gitf52c4f1a

• torchaudio: 2.8.0+rocm7.0.0.git6e1c7fe9

Python Environment:

• Python 3.12.3

• All ComfyUI dependencies successfully installed

🎯 Performance Notes

• Tested Workflow: Wan2.2 image-to-video

• Resolution: 640×640 pixels

• Frames: 81

• GPU: RX 6900 XT (gfx1030)

• Status: Stable and fully functional

💡 Pro Tips

1. Reboot after group changes to ensure permissions take effect

2. Always source your virtual environment before running ComfyUI

3. Check rocminfo output to confirm GPU detection

4. The LD_LIBRARY_PATH export is essential - add it to your .bashrc for persistence

This setup has been thoroughly tested and provides a solid foundation for AMD GPU AI workflows on Ubuntu 24.04. Happy generating!Complete ROCm 7.0 + PyTorch 2.8.0 Installation Guide for RX 6900 XT (gfx1030) on Ubuntu 24.04.2After
extensive testing, I've successfully installed ROCm 7.0 with PyTorch
2.8.0 for AMD RX 6900 XT (gfx1030 architecture) on Ubuntu 24.04.2. The
setup runs ComfyUI's Wan2.2 image-to-video workflow flawlessly at
640×640 resolution with 81 frames. Here's my verified installation
procedure:🚀 PrerequisitesFresh Ubuntu 24.04.2 LTS installation

AMD RX 6000 series GPU (gfx1030 architecture)

This setup has been thoroughly tested and provides a solid foundation for AMD GPU AI workflows on Ubuntu 24.04. Happy generating!

During the generation my system stays fully operational, very responsive and i can continue

-----------------------------

I have a very small PSU, so i set the PwrCap to use max 231 Watt:
rocm-smi

=========================================== ROCm System Management Interface ===========================================

===================================================== Concise Info =====================================================

Device Node IDs Temp Power Partitions SCLK MCLK Fan Perf PwrCap VRAM% GPU%

(DID, GUID) (Edge) (Avg) (Mem, Compute, ID)

========================================================================================================================

0 1 0x73bf, 29880 56.0°C 158.0W N/A, N/A, 0 2545Mhz 456Mhz 36.47% auto 231.0W 71% 99%

========================================================================================================================

================================================= End of ROCm SMI Log ==================================================

-----------------------------

got prompt

Using split attention in VAE

Using split attention in VAE

VAE load device: cuda:0, offload device: cpu, dtype: torch.float16

Using scaled fp8: fp8 matrix mult: False, scale input: False

Requested to load WanTEModel

loaded completely 9.5367431640625e+25 6419.477203369141 True

CLIP/text encoder model load device: cuda:0, offload device: cpu, current: cuda:0, dtype: torch.float16

Requested to load WanVAE

loaded completely 10762.5 242.02829551696777 True

Using scaled fp8: fp8 matrix mult: False, scale input: True

model weight dtype torch.float16, manual cast: None

model_type FLOW

Requested to load WAN21

0 models unloaded.

loaded partially 6339.999804687501 6332.647415161133 291

100%|███████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [07:01<00:00, 210.77s/it]

Using scaled fp8: fp8 matrix mult: False, scale input: True

model weight dtype torch.float16, manual cast: None

model_type FLOW

Requested to load WAN21

0 models unloaded.

loaded partially 6339.999804687501 6332.647415161133 291

100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 2/2 [06:58<00:00, 209.20s/it]

Requested to load WanVAE

loaded completely 9949.25 242.02829551696777 True

Prompt executed in 00:36:38 on only 231 Watt!

I am happy after trying every possible solution i could find last year and reinstalling my system countless times! Roc7.0 and Pytorch 2.8.0 is working great for gfx1030

executed in 00:36:38 on only 231 Watt!

I am happy after trying every possible solution i could find last year and reinstalling my system countless times! Roc7.0 and Pytorch 2.8.0 is working great for gfx1030

https://gist.github.com/kundeng/7ae987bc1a6dfdf75175f9c0f0af9711

Install ROCm PyTorch on Windows with AMD Radeon (gfx1151/8060S) – Automated PowerShell Script

Getting ROCm-enabled PyTorch to run natively on Windows with AMD GPUs (like the Radeon 8060S / gfx1151) is tricky: official support is still in progress, wheels are experimental, and HIP runtime setup isn’t obvious.

This script automates the whole process on Windows 10/11:

• Installs uv and Python 3.12 (via winget + uv)

• Creates an isolated virtual environment (.venv)

• Downloads the latest ROCm PyTorch wheels (torch / torchvision / torchaudio) directly from the scottt/rocm-TheRock GitHub releases

• Enforces numpy<2 (the current wheels are built against the NumPy 1.x ABI, so NumPy 2.x causes import errors)

• Installs the AMD Software PRO Edition for HIP (runtime + drivers) if not already present

• Runs a GPU sanity check: verifies that PyTorch sees your Radeon GPU and can execute a CUDA/HIP kernel

Usage

Save the script as install-pytorch-rocm.ps1.

1. Open PowerShell, set execution policy if needed:

   Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy RemoteSigned

2. Run the script:

   .\install-pytorch-rocm.ps1

3. Reboot if prompted after the AMD Software PRO Edition install.

4. Reactivate the environment later with:..venv\Scripts\Activate.ps1

Example Output

Torch version: 2.7.0a0+git3f903c3
CUDA available: True
Device count: 1
Device 0: AMD Radeon(TM) 8060S Graphics
Matrix multiply result on GPU:
 tensor([...], device='cuda:0')

This gives you a working PyTorch + ROCm stack on Windows, no WSL2 required. Perfect for experimenting with training/fine-tuning directly on AMD hardware.