Ollama for AMD Integrated GPUs in Windows

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Overview

If you try and run Ollama with an integrated GPU most likely it will run on the CPU only. It seems some AMD GPUs are supported, but many are not. If you are having issues getting your AMD GPU to work with Ollama you can build a custom version with the latest HIP SDK to enable GPU with Ollama.

What you’ll need:

• A system with AMD Radeon graphics
• Windows 10/11 (Linux it appears has a much easier time)
• At least 16GB of system RAM (more is better for larger models)
• ~20GB of free storage space for Ollama and models

The following are some required guidelines to keep in mind from the start.

Not following the above will result in a painful experience. Trust me.

Install Go

Navigate to the Go download page  and download the latest version of Go.

Once the installer loads, click “Next” to begin. Then click “Next” to accept the EULA. Accept the defaults and then allow the app to make changes.

The installer will complete and then close. No further action is needed.

Install MinGW

Navigate to the MinGW releases page . Select the one similar to x86_64-15.2.0-release-win32-seh-ucrt-rt_v13-rev0.7z.

Now open up the downloads folder and extract the archive.

Update the extract folder to C:\. Then click “Extract”. It can take some time to extract.

Add C:\mingw64\bin to your path by searching for “system” in the Windows search bar and selecting the “Edit the system environment variables” item.

Then click the “Environment Variables…” button.

Then select the “Path” Variable in the top section, and then click the “Edit” button.

Then click “New” and paste in:

C:\mingw64\bin

And click “OK” to save.

Now click “OK” to close the Environment Variable windows that were just opened. MinGW is now ready.

Install Adrenaline Drivers

Navigate to the Adrenaline Drivers download page  and click “Download Windows Drivers”

Run the installer and allow the app to make changes

Accept the defaults and proceed

Let the installer complete, click “Close”, and then proceed to the next step.

Install Git

Navigate to the git-scm download page  and click “Click here to download”.

As usual, select “Yes” to allow the app to make changes. Then leave all the defaults and click “Next” through the entire installation and then click “Finish”.

Install HIP 6.4.2 SDK

Navigate to the HIP SDK download page  and download the 6.4.2 ROCm Version.

Accept the terms.

Start the install and allow the app to make changes to the device. Then keep the defaults and proceed.

Validate and set HIP PATHs

Search for “system” again in the Windows search bar to edit the enviroment variables. Then confirm the two HIP variables are present.

Now add HIP to the PATH similar to with MinGW.

C:\Program Files\AMD\ROCm\6.4\bin

Replace ROCm Library Files

Navigate to the ROCmLib repo  and download libs for your gfx code. If you are not sure, check out the AMD-GPU-Codenames wiki .

For the 680M I will get the gfx1035.

Extract the files and open the folder. There should be a library folder and rocblas.dll.

Open C:\Program Files\AMD\ROCm\6.4\bin\rocblas and rename the existing library folder to library-backup.

Click “continue” to proceed.

Once the folder is renamed, copy over the new library folder.

Now navigate up one folder to C:\Program Files\AMD\ROCm\6.4\bin and paste in the rocblas.dll file, when prompted, replace the existing file.

Install Build Tools

Navigate to the build tools download page  and click “Download Build Tools”.

Run the installer, and allow the app to make changes to the device. Then click “Continue” to start. On the install screen, scroll down to “Desktops & Mobile”, and then select the “Desktop development with c++” (NOTE: The below image was taken after I completed the installation. The lower red box will say “Install” instead of “Close”)

Let the install complete (can take some time).

Install CMake

Navigate to the CMake download page  and find the Windows binary.

Leave the defaults and complete the installation.

Install Inno

Navigate to the Downloads Page 

Install with defaults.

Prepare the Ollama Repo

The next step is to clone the Ollama repo to C:\. Open up powershell and navigate to C:\

cd C:\

Then clone the repo:

git clone https://github.com/ollama/ollama.git

Next, update the FILTER AMDGPU_TARGETS to reflect your GPU. Open up “Notepad” and then open the CMakeLists.txt file.

Identify the section which has FILTER AMDGPU_TARGETS INCLUDE REGEX

Then update it with

list(FILTER AMDGPU_TARGETS INCLUDE REGEX "^gfx(803|900(:xnack-)|902|90c(:xnack-)|1010(:xnack-)|1011|1012(:xnack-)|103[0-6]|110[0-3]|1150)$")

Then save the changes. Using Notepad, select the option for “all files”, then open the CMakePresets.json file.

Find the AMDGPU_TARGETS section.

Update it with your specific GPU type from before (multiple can be added if you want to build for multiple GPUs).

Save the JSON file and then close Notepad.

Create the Installer

Open powershell then run the build script to create the installer.

powershell -ExecutionPolicy Bypass -File .\scripts\build_windows.ps1

The result will be an installer in the /dist folder

Install Ollama

Navigate to the /dist folder:

cd C:\ollama\dist

folder and then run the installer.

OllamaInstaller.exe

This will begin the installation process.

Verity and Setup Ollama

Ollama should now be running in the background, however I recommend closing it and manually starting by opening up a powershell terminal and running

ollama serve

Then download a model to test

ollama pull llama3.2:1b

By default the full model does not get offloaded to the GPU. To offload the full model you can use a ModelFile.

This sets the num_gpu to 99 ensuring the GPU is fully utilized.

@"
FROM llama3.2:1b
PARAMETER num_gpu 99
"@ | Out-File -FilePath Modelfile -Encoding utf8

Then create the Modelfile

ollama create llama3.2-gpu -f Modelfile

Now to launch the model using the Modelfile run:

ollama run llama3.2-gpu