ROCM FOR AMD RADEON: AI DEVELOPMENT ON CONSUMER GPUS: Run PyTorch, LLMs, and Stable Diffusion on RX 7000/9000 Series with Native Windows and Linux Support

Run real AI workloads on consumer Radeon GPUs with a clear, reproducible ROCm playbook that actually matches how developers work.

Many developers want to run PyTorch, LLMs, and Stable Diffusion on RX 7000 and RX 9000 cards, but end up lost in partial guides, version mismatches, and fragile installs that break after the next driver update.

This book gives you a complete, stack aware view of ROCm on consumer Radeon across Linux, native Windows, and WSL, then walks you through proven workflows for LLM inference, high throughput serving, and diffusion pipelines that stay stable under real use.

• Understand the ROCm stack, support boundaries, and how gfx targets map to RX 7000 and RX 9000 cards
• Plan CPU, RAM, storage, VRAM, and thermals so LLM and diffusion workloads fit and stay stable
• Set up ROCm drivers and PyTorch cleanly on Linux, native Windows, and WSL, with verification workflows
• Install and tune PyTorch on ROCm, from version pinning and wheels to precision choices and Triton kernels
• Run LLMs with llama cpp and vLLM, including quantization tradeoffs, GPU offload, batching, and serving
• Use Hugging Face Transformers on ROCm, manage KV cache behavior, and choose safer quantization paths
• Build Stable Diffusion and ComfyUI pipelines on Radeon, then refine performance, quality, and MIGraphX acceleration
• Go beyond PyTorch with JAX, Triton, TensorFlow, and ONNX plus MIGraphX for portable inference workflows
• Profile and debug issues methodically, from illegal memory access and hangs to mixed driver states and rollbacks

The book includes practical extras such as quick start recipes for Linux, native Windows, and WSL, version pinning templates and requirements files, compatibility checklists for RX 7000 and RX 9000 upgrades, and a troubleshooting index organized by symptom, cause, and fix path.

It is a code heavy guide, with working scripts, commands, and configuration snippets that you can drop into your own environments to validate devices, benchmark kernels, launch servers, and keep model caches and containers reproducible.

Grab your copy today and turn your consumer Radeon card into a reliable AI development platform.