OpenVINO LLMOps: 4-Bit Model Quantization and CPU Inference

Deploying large language models efficiently without dedicated GPU infrastructure is one of the most practical challenges in modern AI operations. This text-based guide teaches you how to optimize, quantize, and serve state-of-the-art models on standard CPU hardware using the OpenVINO toolkit. You will gain a solid understanding of LLMOps workflows, compression strategies, and inference optimization. By reading step-by-step conceptual walkthroughs and examining detailed code snippets, you will discover how to transform heavy foundational models into memory-efficient, performant assets tailored for production environments. What you'll learn: - Understand core LLMOps principles and the role of model optimization frameworks - Master INT4 weight quantization concepts and their impact on memory and inference speed - Convert and optimize large language models like Llama using OpenVINO tools - Configure CPU inference pipelines for minimal latency and lower resource consumption - Apply modern pipeline practices to evaluate accuracy, throughput, and performance trade-offs - Implement text generation loops using structured Python code examples The material begins with fundamental definitions of quantization, model architectures, and LLMOps lifecycle concepts before progressing to hands-on configuration steps and practical implementation scripts. This course is designed for aspiring AI engineers, developers, and tech enthusiasts curious about efficient local model deployment. No prior quantization experience is required. Start reading today to build efficient, optimized language model workflows directly on your CPU hardware.