Stop Wasting GPUs: Building a Global Compute Cache for AI Inference

With the explosion of AI models, keeping every model constantly loaded across every global region is no longer viable: it leaves expensive GPUs sitting idle, plummets utilization, and skyrockets costs. Conversely, centralizing your inference infrastructure destroys the user experience by introducing massive latency spikes. How do you architect a globally distributed AI inference platform that balances low latency with high GPU efficiency?
This session explores a modern approach to solving this dilemma: treating inference like a global compute cache rather than a static system.

We will break down the architectural patterns needed to make this possible, focusing on dynamic model caching, intelligent request routing, and distributed global infrastructure.