Introduction to Large Language Model Training and Parallelism

Training modern large language models requires immense computational power, often exceeding the limits of a single GPU. Understanding how to distribute this workload efficiently is the key to training massive models without running out of memory or waiting weeks for results.\n\nThis text-based course guides you through the core concepts of distributed training, explaining how to split models and data across multiple processors. You will transition from running simple single-GPU scripts to understanding the architecture behind massive multi-node training runs.\n\nWhat you'll learn:\n- Understand foundational concepts of distributed training and why standard hardware limits LLM scaling\n- Configure Data Parallelism (DP) and Distributed Data Parallelism (DDP) to split training data across multiple GPUs\n- Implement Tensor Parallelism to split individual layers and weight matrices across devices\n- Apply Pipeline Parallelism to partition model layers sequentially across a training cluster\n- Explore advanced optimization strategies like Fully Sharded Data Parallel (FSDP) and ZeRO memory optimization\n- Analyze communication bottlenecks and learn how to optimize throughput during training runs\n\nYou will begin with fundamental terminology and hardware constraints before moving step-by-step through data, tensor, and pipeline parallelism. Each concept is reinforced with clear written explanations and structured Python code examples.\n\nThis course is designed for aspiring AI engineers, data scientists, and software developers who want to understand the mechanics of scaling model training. Basic familiarity with Python and neural networks is helpful, but no prior experience with distributed systems is required.\n\nStart reading today to unlock the techniques used to train the world's most powerful AI models.