Designing distributed AI inference: Core concepts and scaling dimensions

This blog post discusses core concepts and scaling dimensions essential for designing distributed AI inference systems. It emphasizes the importance of selecting a model-serving engine and addresses the dynamics between prefill and decode phases in AI workloads. The post covers critical KPIs, innovations in KV cache management, and various parallelism strategies, providing a framework to optimize enterprise AI model deployment and performance.