Continuous batching capability in LLM inference — from attention to KV caching

AI Impact Summary

The change outlines a capability shift toward continuous batching in LLM serving, deriving throughput benefits from overlapping prefill and decode across conversations and from KV caching in attention. It highlights how Q/K/V matrices can differ in token counts across concurrent requests, enabling multi-stream processing to improve utilization of hardware under load. For a technical team, this implies updating the inference stack to support mixed-length sequences, shared QKV caches, and correct causal masking across in-flight requests to preserve token prediction correctness. The business consequence is higher concurrent throughput and lower tail latency for multi-user chat workloads, contingent on implementing robust cache management and batch orchestration in the serving layer.