AI Inference with NIM and LLaMA

Deploying state-of-the-art language models efficiently requires understanding modern inference pipelines and optimized microservices. This written course provides a step-by-step introduction to serving large language models using NIM microservices and running inference on foundational models like LLaMA.\n\nYou will build a practical understanding of how containerized AI endpoints operate, how to interact with model APIs, and how to structure production-ready AI workflows.\n\nWhat you'll learn:\n- Understand core AI inference terminology and microservice architectures\n- Configure and connect to NIM microservices for optimized model serving\n- Construct structured prompts and API calls for LLaMA instruct models\n- Apply retrieval-augmented generation patterns and context handling techniques\n- Evaluate latency, throughput, and resource requirements for large language models\n\nThe reading begins with foundational concepts and API mechanics before progressing into practical code integrations and system architectural patterns. Designed for developers and technical beginners, no prior deep learning background is required to get started. Begin reading today to build reliable, scalable AI inference solutions.