As generative AI pushes deeper into enterprise workflows, the need for flexible, cost-efficient, and controllable deployment is stronger than ever. This talk explores how modern toolchains and optimizations make it possible to run high-performing LLMs and multimodal models entirely on AI PCs. The session breaks down the key challenges of running advanced generative models on consumer-grade hardware—from INT4 quantization strategies and efficient inference with OpenVINO to deployment using OpenVINO Model Server. A live demo will be presented and then dissected end-to-end, showing exactly how it’s built and how anyone can run it at home using the fully open-source implementation. Attendees will leave with a clear, hands-on understanding of how to build, optimize, and deploy advanced AI systems without relying on the cloud - and how to do it entirely on their own terms.
AI Software Evangelist at Intel. Adrian graduated from the Gdansk University of Technology in the field of Computer Science 9 years ago. After that, he started his career in computer vision and deep learning. As a team leader of data scientists and Android developers, Adrian was responsible for an application to take a professional photo (for an ID card or passport) without leaving home. He is a co-author of the LandCover.ai dataset, creator of the Debug Image Viewer Plugin, and a Deep Learning lecturer occasionally. His current role is to educate people about OpenVINO Toolkit.
