Why Inference Is the Defining Layer of AI | CoreWeave

This post shifts the focus from AI training to AI inference, highlighting its growing importance and the unique challenges posed by agentic AI workloads. It details how agentic inference compounds complexity through persistent GPU reservation, unpredictable I/O, high token generation, downstream dependencies, and concurrent agent coordination. The post also addresses systemic issues in production inference, such as cold starts, traffic spikes, observability gaps, and cost drift, and positions CoreWeave's full-stack infrastructure approach as a solution, emphasizing bare-metal GPUs, high-speed networking, AI Object Storage, and a flexible platform migration path from prototype to production.