KV cache management coordinates key and value tensor states across heterogeneous memory tiers and distributed compute nodes to maximize inference throughput while bounding Time-to-First-Token and Inter-Token Latency. In autoregressive transformer inference, high-concurrency serving systems rely on placement, offloading, and migration strategies to avoid failures and maintain performance. Effective KV cache handling is essential for scalable LLM serving in distributed environments.