A beginner's guide to deploying LLMs with AMD on Windows using PyTorch (gpuopen.com)

AMD announced a public preview of native PyTorch support for Radeon GPUs on Windows, plus AMD‑optimized ONNX models on Hugging Face, making it straightforward to run LLMs locally on consumer hardware. The guide demonstrates setting up a Windows 11 PC with Python 3.12, creating a virtual environment, installing a ROCm‑built PyTorch along with Hugging Face Transformers and Accelerate, and running models such as Llama 3.2 1B (including an interactive chat loop). This removes the previous need for Linux-only workflows, dual‑booting or complex workarounds and highlights performance potential on Radeon RX 7000/9000 series and select Ryzen AI 300/AI Max APUs (e.g., Ryzen AI Max variants). Key technical points and implications: supported stack includes Windows 11, Python 3.12, ROCm‑compatible PyTorch build, Transformers and Accelerate; typical steps are python -m venv, activate, pip install the ROCm PyTorch package, then download and run an LLM (first run downloads model weights). Expect a one‑time model download of several GB and a non‑blocking warning about missing “Memory‑Efficient Attention” (current Windows build falls back to the standard implementation). For the AI/ML community this democratizes LLM inference on desktop AMD hardware, accelerates local experimentation and deployment, and signals broader vendor support for end‑user model hosting and optimization on Windows.