AI Infrastructure Explained (GPUs, vLLM, and LLM-D)

AI infrastructure has become a major engineering priority as hyperscalers pour more than $700 billion into systems for model serving, pushing demand for expertise in GPUs, model servers, and Kubernetes-based orchestration. Spending surge reshapes infrastructure demand Amazon, Google, Microsoft, and Meta are projected to spend over $700 billion on AI infrastructure this year, roughly four times their level of four years ago and nearly 80% higher than the previous year. McKinsey expects close to $7 trillion to flow into data centers by 2030, making the buildout of compute, memory, networking, and operations a central industry priority.

Sources from the briefing

• AI Infrastructure Explained (GPUs, vLLM, and LLM-D)KodeKloud