LLM Architecture Design: Compare and Analyze Model Architectures

Selecting the right large language model architecture is the foundation of building efficient, cost-effective, and powerful AI applications. With various model families and structural variations available today, understanding how they differ is crucial for making informed engineering decisions. This text-based course equips you with the conceptual framework needed to analyze, compare, and design LLM architectures for real-world deployment. You will learn to evaluate model trade-offs, from attention mechanisms to parameter efficiency, through structured explanations and written design scenarios. What you'll learn: Understand the foundational building blocks of Transformer architectures, including encoder and decoder variations; Analyze modern attention mechanisms such as Multi-Head Attention and Grouped-Query Attention (GQA); Compare performance trade-offs regarding context window scaling, latency, and memory footprints; Evaluate how architectural choices impact Retrieval-Augmented Generation (RAG) and fine-tuning strategies; Apply systematic selection frameworks to choose the optimal model for specific enterprise use cases. The course starts with essential terminology and foundational structural concepts before moving into comparative analysis of modern model families. You will progress through written case studies and architecture walkthroughs that simulate real-world design decisions. This course is designed for software developers, technical product managers, and aspiring AI engineers who want to understand the structural side of LLMs. No prior background in deep learning mathematics is required. Read through the modules to confidently choose and design your next AI system architecture.