Transformer-based LLMs consistently become the performance choke point in production systems, with latency often exceeding user expectations by 10x. This deep dive reveals how the architecture's design for parallel training clashes with the sequential demands of token generation, creating fundamental bottlenecks rooted in memory access patterns rather than raw compute power. Understanding these constraints is essential for effective optimization.