How distributed inference keeps large AI models moving

Distributed inference frameworks coordinate multiple model servers to efficiently handle large language model requests at scale, optimizing resource use and response times. Techniques like prefix-cache-aware routing and KV-cache offloading improve performance by managing context and memory across servers. The open-source llm-d framework exemplifies these innovations in a Kubernetes-native environment.