Scaling Laws for Large Language Models: Core Concepts and Trade-Offs

Scaling up large language models is highly effective, but training them without understanding resource trade-offs can lead to massive waste. To build or fine-tune modern AI models efficiently, you must grasp the mathematical relationships governing compute, data, and model parameters. This course equips you with a clear, conceptual understanding of how scaling laws predict model performance. You will learn how to analyze resource trade-offs, enabling you to design efficient training runs and select the best model configurations for your budget. What you'll learn: - Understand the fundamental relationships between compute budget, model parameters, and dataset size - Compare key scaling law frameworks, including Kaplan's power laws and Chinchilla compute-optimal scaling - Analyze how loss scales predictably and how this translates to downstream task performance - Evaluate modern dataset curation strategies and their impact on data-constrained scaling limits - Apply scaling equations to estimate the compute resources required for training specific model architectures - Identify the limitations of scaling laws when applied to complex reasoning and safety-alignment tasks The course begins with foundational definitions of parameters, tokens, and compute metrics before guiding you through historical and modern scaling empirical studies. Through detailed written explanations and clear mathematical examples, you will learn to calculate optimal resource allocations for various training scenarios. Designed for AI practitioners, software developers, and tech leads seeking a foundational understanding of LLM economics, this course requires no advanced mathematics or deep learning prerequisites. Start reading to master the core principles driving modern AI scaling.