OpenVINO for LLMs: Transformer Inference on CPU and GPU

Running large language models efficiently requires specialized optimization techniques to maximize hardware performance across CPUs and GPUs. This text-based course guides you through foundational LLMOps workflows, demonstrating how to accelerate transformer inference using the OpenVINO toolkit. You will gain practical knowledge in preparing, converting, and executing modern language models with low latency and reduced memory footprints. What you'll learn: - Understand core LLMOps concepts, runtime architecture, and inference fundamentals - Convert transformer models from popular model hubs into OpenVINO intermediate representations - Apply model compression techniques such as weight quantization for efficient execution - Configure and run inference workloads targeting both CPU and integrated or discrete GPU devices - Build complete text generation pipelines in Python using optimized runtime APIs - Measure and evaluate critical performance metrics including latency, throughput, and memory consumption The course begins with fundamental terminology and deployment concepts before advancing through practical model conversion, quantization workflows, and production-oriented inference pipelines. Designed for Python developers, data practitioners, and AI enthusiasts, this course requires no prior experience with hardware acceleration or OpenVINO. Begin reading today to build fast, efficient transformer inference pipelines on your hardware.