Disaggregation makes KV cache a system primitive

Inference workloads are evolving faster than the serving architectures designed to support them, leading to a need for disaggregation of the prefill and decode phases, which have different computational requirements. Prefill is compute-intensive and benefits from high-FLOPS accelerators, whereas decode demands large, fast memory and is sensitive to latency and memory bandwidth. By separating these phases onto different machines—prefill nodes for computation and decode nodes for memory management—the interference between them is minimized, yet this introduces new challenges related to managing the KV cache, which serves as the connection between the two. This separation transforms the KV cache from a minor implementation detail into a critical component that must be efficiently transferred, routed, stored, and expired across distributed systems. Solutions such as NVIDIA's Dynamo and AWS's infrastructure developments are addressing these challenges by focusing on disaggregated inference systems, emphasizing the importance of the KV cache in ensuring seamless operation between prefill and decode processes.

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