{"product_id":"rocm-for-amd-radeon-sofia-draycott-9798243428965","title":"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 Sup","description":"\u003cp\u003e\u003cb\u003eRun real AI workloads on consumer Radeon GPUs with a clear, reproducible ROCm playbook that actually matches how developers work.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eMany 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.\u003c\/p\u003e\u003cp\u003eThis 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.\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eUnderstand the ROCm stack, support boundaries, and how gfx targets map to RX 7000 and RX 9000 cards\u003c\/li\u003e\n\u003cli\u003ePlan CPU, RAM, storage, VRAM, and thermals so LLM and diffusion workloads fit and stay stable\u003c\/li\u003e\n\u003cli\u003eSet up ROCm drivers and PyTorch cleanly on Linux, native Windows, and WSL, with verification workflows\u003c\/li\u003e\n\u003cli\u003eInstall and tune PyTorch on ROCm, from version pinning and wheels to precision choices and Triton kernels\u003c\/li\u003e\n\u003cli\u003eRun LLMs with llama cpp and vLLM, including quantization tradeoffs, GPU offload, batching, and serving\u003c\/li\u003e\n\u003cli\u003eUse Hugging Face Transformers on ROCm, manage KV cache behavior, and choose safer quantization paths\u003c\/li\u003e\n\u003cli\u003eBuild Stable Diffusion and ComfyUI pipelines on Radeon, then refine performance, quality, and MIGraphX acceleration\u003c\/li\u003e\n\u003cli\u003eGo beyond PyTorch with JAX, Triton, TensorFlow, and ONNX plus MIGraphX for portable inference workflows\u003c\/li\u003e\n\u003cli\u003eProfile and debug issues methodically, from illegal memory access and hangs to mixed driver states and rollbacks\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eIt 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.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eGrab your copy today and turn your consumer Radeon card into a reliable AI development platform.\u003c\/b\u003e\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Sofia Draycott\u003cbr\u003e\u003cb\u003eISBN-13:\u003c\/b\u003e 9798243428965\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Independently Published\u003cbr\u003e\u003cb\u003eLanguage:\u003c\/b\u003e English\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 01\/10\/2026\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 350\u003cbr\u003e\u003cb\u003eFormat:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.34lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 10.00h x 7.00w x 0.73d","brand":"Sofia Draycott","offers":[{"title":"Paperback","offer_id":48242102436095,"sku":"9798243428965","price":34.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0662\/2982\/9887\/files\/img_4d22b2da-faec-4623-9d6b-369dfd5176c8.jpg?v=1772657541","url":"https:\/\/www.whiterainbookhouse.com\/products\/rocm-for-amd-radeon-sofia-draycott-9798243428965","provider":"WR Book House","version":"1.0","type":"link"}