This course offers an in-depth exploration of parallelism in Large Language Model (LLM) training. Beginning with foundational IT concepts like cloud computing, GPUs, and network communication, the course introduces various parallelism techniques such as data parallelism, model parallelism, hybrid approaches, and pipeline parallelism, explaining their benefits and trade-offs. You’ll then apply these strategies in hands-on demos using real-world datasets like MNIST and WikiText. As you progress, you’ll work on true parallelism with multiple GPUs through platforms like Runpod.io, and dive into essential topics such as fault tolerance, scalability, and checkpointing strategies. These lessons ensure your training systems are resilient and optimized for large-scale machine learning workflows. With insights into GPU architectures and advanced tools like DeepSpeed, you'll be equipped to handle the complexities of training massive models efficiently. Whether you're an AI researcher or a data scientist, this course provides the knowledge and practical experience needed to accelerate LLM training and build scalable, efficient AI systems. Through a combination of theoretical lessons and hands-on applications, you’ll master parallelism techniques and become proficient in building and optimizing high-performance LLM training pipelines.