Running Local AI Workloads on AMD Hardware

Hardware and Software Compatibility

The current AMD AI stack, specifically when paired with ROCm (Radeon Open Compute), has reached a level of maturity where common AI tools and frameworks function with minimal configuration. Using a workstation equipped with a Ryzen Threadripper 9980X and a Radeon AI Pro R9700 GPU (32GB VRAM), standard inference tools like LM Studio and Ollama detect the ROCm runtime automatically. This setup allows for running high-quality open-weight models, such as Qwen 3.6 or Gemma, at 4-bit or 8-bit quantization without significant performance compromises, achieving speeds of approximately 160 tokens per second.

Training and Generative Workflows

Beyond simple inference, the AMD ecosystem supports complex development tasks including fine-tuning and generative media creation. Developers can utilize the official ROCm-optimized PyTorch wheels to run standard transformers-based code and fine-tuning libraries like Unsloth. For generative tasks, ComfyUI supports ROCm, enabling efficient image and video model execution. The author demonstrates that changing seeds and generating variations in ComfyUI remains responsive, confirming the hardware's viability for creative AI pipelines.

The Linux Advantage

While Windows environments are functional, a native Linux installation provides the most stable and performant experience for deep learning workloads. By running Linux, developers gain full access to ROCm 7.2, allowing for direct PyTorch integration where the GPU is recognized as a standard compute device. This environment supports advanced serving frameworks like vLLM and allows for the execution of full-resolution models using the transformers library, providing a reliable alternative to cloud-based APIs for agentic and reasoning-heavy tasks.