# How I Got Ollama Running on My AMD Radeon RX 9060 XT Using WSL and ROCm > Source: > Published: 2026-08-28 12:22:44+00:00 I wanted to start experimenting with AI for a while. At work, I didn't really have enough time to properly explore this new ecosystem and understand how these tools could be used. But I had another resource available: my personal hardware. I'm a gamer, so I have a reasonably powerful desktop PC. I'm also a developer, with a laptop running Ubuntu and a NAS. That made me wonder: Can I use my gaming PC to run AI models locally? The answer is **yes**. This article is a small walkthrough of my experience setting up a local AI environment using an **AMD Radeon GPU**, **Windows**, **WSL**, **ROCm**, and **Ollama**. My hardware is (yes, I'm Team Red): The first step is to make sure that WSL is installed and up to date. The general process is: From PowerShell: ``` # Check the current version of WSL wsl -v # Update WSL wsl --update # Install Ubuntu wsl --install -d Ubuntu # Start Ubuntu wsl -d Ubuntu # Stop Ubuntu wsl --terminate Ubuntu # Remove the Ubuntu distribution wsl --unregister Ubuntu ``` Once Ubuntu is installed, update the system and install `zstd` . We will need it later to extract Ollama. ``` sudo apt update sudo apt upgrade sudo apt autoclean sudo apt autoremove sudo apt install zstd ``` Ollama is the foundation of this setup. It allows us to download and run AI models locally while providing different compute backends, including CPU, CUDA, and ROCm. One thing that wasn't immediately obvious to me was that Ollama provides a specific ROCm backend. The installation is split into two parts: ``` # Install Ollama curl -fsSL https://ollama.com/download/ollama-linux-amd64.tar.zst \ | sudo tar --zstd -x -C /usr # Install the ROCm backend curl -fsSL https://ollama.com/download/ollama-linux-amd64-rocm.tar.zst \ | sudo tar --zstd -x -C /usr ``` Then create the Ollama user and add your current user to the Ollama group: ``` # Create the Ollama user and group sudo useradd -r -s /bin/false -U -m -d /usr/share/ollama ollama # Add the current user to the Ollama group sudo usermod -a -G ollama $(whoami) ``` The official documentation I used: [https://docs.ollama.com/linux#manual-install](https://docs.ollama.com/linux#manual-install) [https://docs.ollama.com/linux#amd-gpu-install](https://docs.ollama.com/linux#amd-gpu-install) I chose to run Ollama as a systemd service. Create or edit the service configuration: ``` sudo nano /etc/systemd/system/ollama.service [Unit] Description=Ollama Service After=network-online.target [Service] ExecStart=/usr/local/bin/ollama serve User=ollama Group=ollama Restart=always RestartSec=3 Environment="PATH=$PATH" # Required to detect the GPU through DXG Environment="HSA_ENABLE_DXG_DETECTION=1" # Optional: expose Ollama outside of WSL Environment="OLLAMA_HOST=0.0.0.0:11434" [Install] WantedBy=default.target ``` Then reload systemd and start the service: ``` sudo systemctl daemon-reload sudo systemctl enable ollama sudo systemctl start ollama.service sudo systemctl status ollama.service ``` More information about running Ollama as a service: [https://docs.ollama.com/linux#adding-ollama-as-a-startup-service-recommended](https://docs.ollama.com/linux#adding-ollama-as-a-startup-service-recommended) The GPU needs to be exposed to the WSL environment. For my setup, I used `librocdxg` . One important detail: version **1.2.2** solved an issue I encountered with version **1.2.0**. So, if you're having issues with GPU detection, the version may matter. ``` wget https://github.com/ROCm/librocdxg/releases/download/v1.2.2/rocdxg-roct_1.2.2_amd64.deb sudo apt install ./rocdxg-roct_1.2.2_amd64.deb ``` More information about the project: [https://github.com/ROCm/librocdxg](https://github.com/ROCm/librocdxg) At this point, everything should be configured. Restart Ollama: ``` sudo systemctl restart ollama.service ``` Then check the Ollama logs: ``` sudo journalctl -u ollama --no-pager -o cat | grep "inference compute" ``` You should see something similar to: ``` library=ROCm compute=gfxXXXX description="[NAME_OF_YOUR_GPU]" ``` The important part is: ``` library=ROCm ``` You don't want Ollama to silently fall back to CPU inference. In my case, I get: ``` time=2026-08-28T12:38:20.900+02:00 level=INFO source=types.go:32 msg="inference compute" id=0 filter_id=0 library=ROCm compute=gfx1200 name=ROCm0 description="AMD Radeon RX 9060 XT" libdirs=ollama,rocm_v7_2 driver=0.0 pci_id=0000:2b:00.0 type=discrete total="15.9 GiB" available="14.2 GiB" ``` As you can see, Ollama detects: `gfx1200` .AMD provides a compatibility matrix to identify which `gfx` version corresponds to your GPU: I also found references suggesting that it may be possible to enable support for some older `gfx` versions, but I didn't need to do this for my setup. Time for the interesting part. Download and run a model: ``` ollama run qwen3:8b ``` Ask the model something and let it generate a response. You can exit the interactive session with: ``` /bye ``` You can then check where the model is running: ``` ollama ps ``` The output should indicate that the model is running on the GPU. ``` NAME ID SIZE PROCESSOR qwen3:8b xxxxxxxxxxxx 5.2 GB 100% GPU ``` You can also open the **Windows Task Manager** and monitor your GPU usage. Once the model starts running, you should see: By default, your Ollama instance is running inside WSL. If you want to access it from other machines on your local network, you need to expose the port through Windows. On the Windows host, open PowerShell as Administrator. First, create a firewall rule: ``` New-NetFirewallRule ` -DisplayName "Ollama WSL LAN" ` -Direction Inbound ` -Protocol TCP ` -LocalPort 11434 ` -Action Allow ` -RemoteAddress [IP_BASE_TO_LISTEN]/24 ``` Then forward the port between Windows and WSL: ``` netsh interface portproxy add v4tov4 ` listenaddress=[IP_HOST_WSL] ` listenport=11434 ` connectaddress=[IP_UBUNTU_IMAGE] ` connectport=11434 ``` From another computer on your local network, you can test the Ollama API with: ``` curl.exe http://[IP_OF_OLLAMA_HOST]:11434/api/tags ``` If everything is configured correctly, Ollama should respond with the list of installed models. By default, WSL may stop after some time when no active terminal is connected. To keep the WSL environment alive, create a `.wslconfig` file in your Windows user profile directory. Add: ``` [general] instanceIdleTimeout=-1 ``` After modifying the configuration, restart the WSL distribution so the configuration is taken into account Now once you started the image you will need to kill it manually. ``` wsl --terminate Ubuntu ``` I encountered one additional issue. After restarting my PC, I could start the WSL distribution, but Ollama was no longer accessible from the local network. In my case, restarting the Windows `iphlpsvc` service solved the problem. Open PowerShell as Administrator: ``` Restart-Service iphlpsvc -Force ``` After that, Ollama became accessible again. This setup allowed me to turn my gaming PC into a local AI machine without replacing Windows or dedicating the entire computer to Linux. The combination of: provides a relatively convenient environment for experimenting with local AI models. The most important part of the setup, in my experience, was making sure that Ollama was actually using the ROCm backend and the GPU. Once that was working, running a model such as `qwen3:8b` was straightforward. Hopefully, this will save some time for other AMD GPU users who want to experiment with local AI on Windows.