As AI models grow to billions and trillions of parameters, distributed systems are essential for training and serving them. Many resources cover fragments of this domain, but none provide a full path from distributed training to inference and production deployment. This book fills that gap with practical, production-focused examples. It starts with GPU and memory estimation, data preparation, and an overview of GPU architecture, interconnects, and core parallelism strategies. You'll learn training techniques including data parallelism for single and multi-node setups, parameter sharding for memory-efficient scaling, and methods to reduce memory usage in large models. The next section covers distributed inference and deployment. You'll build high-performance systems using optimized attention, caching, operator fusion, and router-based designs. You'll deploy on schedulers and container platforms with GPU-aware orchestration and assemble production stacks emphasizing reliability, scalability, and observability. The final section covers benchmarking, performance tuning, and trends like MoE models, edge-cloud coordination, and advanced parallelism. Each chapter includes tested code and debugging guidance. By the end, you'll be able to build distributed AI systems that scale from a single GPU to large clusters.