Guided developer journeys for AI/ML workloads on AMD devices.
This is AMD's official repository of playbooks for AMD developer platforms. Each playbook is a self-contained, hands-on learning experience that covers prerequisites, step-by-step instructions for Windows and Linux, troubleshooting guidance, and working example code, built to help you grow your AI development skills one project at a time.
| Playbook | Description |
|---|---|
| Running LLMs with PyTorch and AMD ROCm™ software | Run powerful language models locally with PyTorch and ROCm |
| Running and Serving LLMs with LM Studio | Set up LM Studio to run and serve large language models |
| Automating Workflows with n8n and Local LLMs | Build an AI-powered news summarizer using n8n and Lemonade |
| Local LLM Coding with VS Code and Qwen3-Coder | Use VS Code with locally-running Qwen3-Coder for private code assistance |
| Generating Images with ComfyUI and Z Image Turbo | Create AI-generated images using ComfyUI with Z Image Turbo |
| Chat with LLMs in Open WebUI | Set up Open WebUI to chat with local LLMs |
| Fine-tune LLMs with PyTorch and AMD ROCm™ software | Fine-tune large language models using PyTorch and ROCm |
| Using Lemonade Across CPU, GPU, and NPU | Learn how to use the Lemonade framework across CPU, GPU, and NPU |
| Optimized Fine-tuning with Unsloth | Memory-efficient LoRA fine-tuning with Unsloth |
| Speech-to-Speech Translation | Build a real-time speech-to-speech translation system |
| Local Computer Vision with Ryzen™ AI NPU | Build local perception capabilities using CVML SDK on Ryzen AI and ROCm |
| Clustering Two Devices with llama.cpp RPC | Distributed inference using RPC server across two AMD devices with llama.cpp |
| Getting Started with Ollama | Install Ollama and run LLMs locally from the terminal, desktop app, or REST API |
| Getting Started Creating Agents with GAIA | Build and deploy AI agents using the GAIA framework |
| Fine-tuning LLMs with LLaMA-Factory | LoRA fine-tuning of large language models using LLaMA-Factory |
| Custom GPU Kernels with PyTorch ROCm | Write and optimize custom GPU kernels using PyTorch and ROCm |
| Quick Start on vLLM | Run inference and serving using vLLM |
| Clustering with RCCL | Multi-node cluster using two AMD devices with RCCL |
We accept playbook proposals from the community, from AMD partners and ISVs, and from AMD employees, and every proposal goes through AMD review before any code is written.
- Found a problem in a playbook? Open a bug report.
- Have an idea for a new playbook? Submit a proposal — a short abstract, not a pull request. AMD maintainers review it and approve or decline it on the issue. Only approved proposals move on to a pull request.
See CONTRIBUTING.md for the full process and what reviewers look for, and the Playbook Creation Guide for how to write one once it is approved.
Join the AMD AI Developer Program →
Get access to tools, resources, and community support to accelerate your AI development on AMD hardware.
- Playbooks Portal: amd.com/playbooks
- AMD Developer Hub: developer.amd.com
- ROCm Documentation: rocm.docs.amd.com
- AMD AI Developer Program: amd.com/ai-dev-program
- AMD Developer Discord: discord.com/invite/amd-dev
- AMD Community Updates: community.amd.com
This project is licensed under the MIT License. See LICENSE for details.