What is AMD ROCm software: Programming AMD GPUs

ROCm is an open, production-grade software platform with drivers, libraries, and developer tools for programming AMD GPUs.

It supports HPC and AI workloads, enabling developers to train and serve models at scale using familiar frameworks like PyTorch.

What's New

AMD AI DevDay World Tour

AMD AI DevDay is a free AI developer event with hands-on workshops, technical talks, and ROCm certification. Register for a city near you.

Run AI Workloads Locally with AMD AI Playbooks

AMD AI Playbooks are free, step-by-step guides covering inference, fine-tuning, agent workflows, and distributed inference - all running locally on AMD client platforms. The AMD Ryzen™ AI Halo ships with pre-configured ROCm support, so you can go from any playbook straight to a running workload with no cloud dependency.

Start Building With ROCm

Optimize AI in the Cloud or Data Center

Large-scale AI workloads running on AMD Instinct™ GPUs

Run AI Applications Locally

Local AI applications using AMD Radeon™ GPUs

Develop HPC Applications

High-performance computing with AMD Instinct GPUs

Find the Right GPU for Your Workload

AMD Instinct GPUs are built for training AI models, running large-scale inference services, and HPC simulations.

AMD Radeon GPUs are built for AI-powered gaming and real-time graphics, creative workflows, and local LLMs.

Move from CUDA to ROCm with Few to No Code Changes

You can run many workloads on AMD GPUs — without changing the code. When changes are needed, the HIPIFY tool helps you convert existing CUDA code to ROCm-compatible HIP code with minimal disruption.

Join the AI Developer Program

Access free AMD Developer Cloud credits, exclusive training, monthly AMD GPU sweepstakes, and community support designed to support your AI development work.

Frequently Asked Questions

ROCm and CUDA are both platforms for programming GPUs, but they differ in their approach.

ROCm is a full open-source, production-ready software stack compatible with a broad range of AMD GPUs. It supports robust tools, AI models, and frameworks, including PyTorch, TensorFlow, JAX, and more. The platform offers the flexibility to customize, modify, and optimize for specific environments and requirements — and is supported by a vibrant open ecosystem for faster innovation.

CUDA is NVIDIA’s proprietary platform, tightly integrated with its hardware. While CUDA is mature, its closed ecosystem can lock in developers and limit choice.

ROCm is primarily used for GPU-accelerated AI and HPC workloads, especially deep learning training, fine-tuning, and inference across frameworks like PyTorch, TensorFlow, and JAX. Key use cases include: