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 VSCode and Qwen3-Coder | Use VSCode 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 |
| Playbook | Description |
|---|---|
| Chat with LLMs in Open WebUI | Set up Open WebUI to chat with local LLMs |
| Fine-tune LLMs with PyTorch and ROCm | 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 |
| 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 |
| Optimized Fine-tuning with Unsloth | Memory-efficient LoRA fine-tuning with Unsloth |
| Quick Start on vLLM | Run inference and serving using vLLM |
| Clustering with RCCL | Multi-node cluster using two AMD devices with RCCL |
| Speech-to-Speech Translation | Build a real-time speech-to-speech translation system |
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
- Community Forum: community.amd.com
This project is licensed under the MIT License. See LICENSE for details.