Scaling large language models to handle billions of requests with low latency isn't just about throwing more GPUs at the problem—it involves secretive optimizations, custom hardware, and trade-offs that remain closely guarded by giants like OpenAI. This article explores the elusive engineering tricks, from bare-metal CUDA hacks to clever load balancing, and why cost and secrecy dominate the high-stakes AI infrastructure race.