LLMOps with OpenVINO: INT4 Quantization and CPU Inference

Deploying large language models efficiently on standard hardware is an essential capability in modern machine learning operations. This text-based course guides you through model compression and deployment, giving you the practical knowledge needed to quantize and serve modern language models like LLaMA directly on CPUs. You will read through clear explanations, conceptual breakdowns, and code implementations designed to make high-performance local inference accessible without requiring specialized hardware. What you'll learn: - Understand foundational LLMOps concepts, model architectures, and inference bottlenecks - Configure OpenVINO pipelines to convert and optimize modern open-weights language models - Apply INT4 post-training quantization to drastically reduce memory footprints while preserving model accuracy - Implement efficient text generation routines tailored for CPU inference - Measure and evaluate key performance metrics including latency, throughput, and memory consumption - Integrate quantized models into practical Python workflows and lightweight service endpoints The course begins with fundamental terminology and quantization theory before stepping through model conversion, calibration, and inference execution. It is suited for programmers, data scientists, and machine learning learners looking to optimize and deploy models locally, with only basic Python familiarity required. Start reading to master cost-effective model deployment techniques.