openvino_notebooks

Why we included this project

For anyone trying to get a deep learning model running efficiently on ordinary hardware like Intel CPUs, integrated GPUs, or the newer NPUs, this collection of Jupyter notebooks is one of the most practical starting points around. Instead of reading docs in the abstract, you get ready-to-run examples that walk through converting a model to OpenVINO's Intermediate Representation, quantizing it, and measuring the speedup. The notebooks cover a wide range of modern workloads, with worked demos for classic computer vision tasks alongside newer text-to-image, video-generation, and LLM examples, each showing how to bring that model onto OpenVINO. That range makes the collection useful both as a learning path for engineers new to the toolkit and as a reference to adapt when your own project needs optimized local inference. Think of it as a curated tutorial library and pattern collection rather than something you deploy directly, and it will save you plenty of trial and error.