Run Smaller Models Locally on AMD Radeon™ GPUs

Experiment with using AMD ROCm™ for AMD Radeon GPU programming. Find sample playbooks, local AI development tools, tutorials, open-source projects, documentation, and more.  

Install ROCm:

Linux
Windows
WSL

See the Prerequisites

Run Sample Local AI Workloads

Try out the ROCm software stack for edge AI use cases.

Access All Playbooks

Featured Workloads

Get Access to AMD Radeon GPUs

Want to Test Drive AMD Radeon GPU programming with ROCm?

Experiment with powerful AMD Radeon GPUs for developing local AI applications based on your geography.

Explore Radeon AI PRO Cards
AMD Radeon™ AI PRO R9700 and R9700S

Learning Resources

Explore more topics to find relevant learning resources to help you stay updated with the latest technological advancements from AMD.

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Frequently Asked Questions

Developing local AI applications on GPUs is ideal when you need low latency, real-time responsiveness, cost control, or data privacy without relying on the cloud.  By programming AMD Radeon GPUs with ROCm, developers can run AI workloads locally for use cases such as:

  • Local LLMs and copilots for retrieval-augmented generation (RAG), coding assistance, and private AI applications
  • Creative and media workflows like image generation, video and audio enhancement, and 3D rendering
  • AI-powered gaming and real-time graphics 

Successive ROCm releases have seen significant leaps in performance. See the performance results page to get a reference point for evaluating how AMD Instinct GPUs perform with ROCm software running popular AI models for inference (e.g., vLLM, xDiT) and training (e.g., PyTorch, Megatron-LM, and JAX MaxText).

AMD Radeon RX, Radeon PRO, and Radeon AI Pro GPUs with ROCm support offer powerful local AI fine-tuning and inference. PyTorch can be installed for ROCm on these GPUs using PIP (for Linux and Windows) or Docker (for Linux) installation methods. The AMD ROCm team actively contributes to open-source development and collaborates closely with framework organizations, including PyTorch.