For years, much of the AI industry's attention has been on building larger models, buying more GPUs, and expanding data centers. But model size and hardware capacity are only part of the equation. What matters just as much is how efficiently those resources are used. Running a large language model at scale involves more than loading weights into GPU memory. There's request scheduling, memory management, communication between devices, caching, and the constant challenge of keeping expensive...