Scaling AI inference to handle millions of requests isn't just about raw compute power—it's an engineering challenge.

A major cloud provider recently demonstrated how they're running NVIDIA's Dynamo framework in production. The setup handles real-time ad bidding with sub-100ms latency requirements while processing massive throughput.

The interesting part? How they balance cost, performance, and reliability when your AI models need to respond faster than users can blink. Techniques like model quantization, batching strategies, and specialized instance types all come into play.

For Web3 projects building AI-powered features, these infrastructure patterns matter—whether you're doing on-chain analytics or running recommendation engines.