Operating Distributed Inference Systems at Scale — Nishant Gupta & Naman Ahuja, Meta
Meta already handles more inference requests than the world's largest microservices, and the volume is growing faster than any workload they run.
Nishant Gupta compares the evolution to cloud infrastructure in 2008: value shifted from individual virtual machines to orchestrators that manage complexity. The same shift is happening for AI inference in just a few years. What is new is coupling between layers—a routing decision affects cache hit rates, which affects GPU load, which affects autoscaling. Naman Ahuja explains that inference needs its own control plane, much like virtual machines needed Kubernetes.
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