LLM and Generative AI Deployment with NVIDIA: Associate Exam Prep

Deploying large language models efficiently requires specialized hardware acceleration and software optimization. This text-based course guides you through the foundational concepts of serving generative AI models using industry-standard NVIDIA technologies. You will transition from understanding basic model architectures to configuring and deploying high-performance inference pipelines. Through structured explanations, architectural breakdowns, and configuration walkthroughs, you will gain the practical knowledge needed to optimize models for production and prepare confidently for associate-level deployment exams. What you will learn: Understand the core architecture of large language models and generative AI deployment pipelines; Configure Triton Inference Server for scalable, multi-model serving; Optimize model performance using TensorRT-LLM and modern quantization techniques; Deploy retrieval-augmented generation (RAG) workflows for production environments; Monitor and troubleshoot model latency, throughput, and hardware utilization; Practice with exam-aligned concepts to build confidence for associate-level certification. The course starts with essential terminology and the fundamentals of hardware-accelerated inference before moving into hands-on configuration scenarios. You will explore practical deployment strategies, performance tuning, and optimization patterns through clear, written explanations and configuration examples. This course is designed for aspiring AI engineers, system administrators, and developers looking to enter the field of AI operations; no prior deployment experience is required, though basic familiarity with AI concepts is helpful. Start reading today to master the fundamentals of high-performance generative AI deployment.