Operating Distributed Inference Systems at Scale — Nishant Gupta & Naman Ahuja, Meta

Operating distributed inference systems at scale requires a fundamental shift from simple model serving to a robust, orchestration-driven control plane. As agentic workloads grow, capacity planning must move beyond linear scaling to account for complex variables like KV cache management, heterogeneous hardware, and multi-step workflow dependencies. Optimizing for "successful tasks" rather than just cost-per-token is essential, necessitating a holistic approach that integrates routing, admission control, and workload-aware scheduling. Because inference behaves like a distributed transaction, reliability must be built into the control plane to manage cascading failures and partial state loss. Ultimately, the next phase of AI infrastructure lies in treating models, memory, and compute as unified resources within a sophisticated orchestration layer, mirroring the evolution of cloud computing from virtual machines to complex service meshes.