Disaggregation Makes KV Cache a System Primitive

Disaggregation of prefill and decode workloads in inference systems is becoming a crucial architectural shift, driven by the need to optimize both compute-heavy and latency-sensitive tasks. By separating these phases and using a KV cache transfer interface, systems can better manage workload-specific hardware requirements, as prefill and decode can operate on different nodes optimized for their respective needs. This separation transforms KV cache from an implementation detail into a fundamental distributed systems primitive, requiring new considerations for transfer latency, cache placement, and lifecycle management. Companies like NVIDIA and AWS are leading this transition by integrating disaggregated inference into their infrastructure, highlighting the economic and performance benefits of using hardware tailored to specific workload demands. As KV cache management becomes more prominent, techniques that reduce cache size enhance the practicality of disaggregation, reinforcing its importance in modern inference architectures.