How I Made My AMD RX6750XT Work for Machine Learning (Despite ROCm Limitations)

The GPU Regret: AMD vs Nvidia for AI Development

The 2020–2022 crypto boom made buying GPUs a nightmare, especially Nvidia’s RTX series. Back then, Team Red’s RX6000 lineup felt like a steal. I grabbed an AMD RX6750XT—same chip as the RX6700XT (gfx1031)—and skipped ray tracing for affordable rasterization. "Price over pixels," right?

Fast forward to 2024. Crypto’s fading, but the AI boom exploded. As a developer itching to experiment with LLMs and PyTorch, I realized my mistake: Nvidia’s CUDA dominance leaves AMD GPUs in the dust. ROCm—AMD’s answer to CUDA—officially supports RX6800XT (gfx1030) but snubs RX6700XT/6750XT (gfx1031). Cue the regret.

If I could time-travel, I’d slap my 2022 self and yell: “Buy an RTX 3070 instead!” But since I’m stuck with my RX6750XT, here’s how I forced it to play nice with machine learning workloads.

Hacking ROCm Support for RX6700XT/6750XT GPUs

Tools Used:

OS: Linux Mint 22.1
GPU: AMD RX6750XT (gfx1031)

Step 1: Install AMD Drivers & ROCm Runtime

Download the AMD amdgpu-install Debian Package for RX6700XT from here.
Install the package (no tutorial here—trust your Googling skills).
Run: amdgpu-install --usecase=graphics,rocm

Step 2: User Permissions & Reboot

Add yourself to the video and render groups:

sudo usermod -a -G video $USER  
sudo usermod -a -G render $USER

Reboot.

Step 3: The GFX1031 vs GFX1030 Spoof

Post-reboot, run rocminfo. You should be able to see your GPU here.

Step 4: Making Sure it Works

Here’s my working Ollama Docker command:

docker run -d --restart always \  
  --device /dev/kfd --device /dev/dri \  
  -v ollama:/root/.ollama -p 11434:11434 \  
  --name ollama \  
  -e HSA_OVERRIDE_GFX_VERSION=10.3.0 \  
  -e HCC_AMDGPU_TARGET=gfx1030 \  
  ollama/ollama:rocm

Final Thoughts

AMD’s improving ROCm, but driver issues and hardware restrictions linger. Hopefully the situation improves for future platforms.

References:

Notes: this blog post was edited by AI.