OpenVINO and LLMOps: CPU Inference for Generative AI

Deploying generative AI workloads without dedicated GPU infrastructure requires efficient model optimization and inference pipelines. This text-based guide teaches you how to leverage OpenVINO to optimize large language and diffusion models for high-performance execution on standard CPUs. You will begin by exploring essential concepts of AI inference, model compression, and modern LLMOps pipelines. Through clear text explanations and code walkthroughs, you will discover how to convert popular models, apply post-training quantization, and build streamlined inference routines. What you will learn: Understand the core architecture and components of the OpenVINO runtime; Convert and optimize generative AI architectures such as Stable Diffusion for CPU deployment; Apply modern quantization techniques including INT8 and INT4 precision reduction; Configure efficient inference pipelines and manage CPU threading for optimal throughput; Implement LLMOps practices for local model serving and performance evaluation. The course begins with fundamental terminology and environment setup before advancing into step-by-step model conversion and deployment exercises. Designed for developers, data practitioners, and AI enthusiasts, no prior hardware acceleration experience is required to get started. Start reading today to build efficient, CPU-ready generative AI solutions.