For any enterprise, the high cost and chronic underutilization of GPUs is the single greatest threat to AI ROI. With one-third of cloud GPUs operating at less than 15% capacity, the key is to stop managing hardware and start delivering value.

This session presents a blueprint for transforming siloed GPU infrastructure into a centralized, high-yield "AI platform." We'll show why Kubernetes is the core economic engine that maximizes the return on your most expensive assets, not just a technical orchestrator. Learn how to:

Create a single, fungible GPU fabric to be shared across teams, boosting utilization.
Use intelligent autoscaling to match infrastructure spend to real-time demand.
Leverage vLLM and a new observability framework for distributed inference on kubernetes, llm-d, to manage performance SLOs (e.g., TTFT, TPS), giving you fine-grained control over token economics through tiered service offerings.

This blueprint is for platform engineers, MLOps leaders, and IT decision-makers aiming to justify AI spend and build an efficient foundation for innovation.