KV Cache-Aware Routing and P/D Disaggregation on Kubernetes: The Parts Public Benchmarks Don't Show

Official Schedule Context

We're at the inflection point between classic LLM inference and agentic inference. When we look at

the agentic workloads and trace replays, many core characteristics break classic LLM serving

assumptions. The most consequential: the server no longer controls its own cache lifecycle. The

client does, through prompt construction, multi-turn context that grows and changes each turn. This

has downstream effects. Because context is client-determined, prefill strategy, eviction, and

routing decisions move up to the scheduler layer. KV cache becomes volatile — frequent eviction and

rewrite, driven from outside the engine. And latency becomes a first-class scheduling metric

alongside throughput. This talk covers the open stack for LLM and agentic era inference serving:

vLLM and llm-d. We begin with the core characteristics and challenges of agentic inference, then

the economics: prefill dominates cost, and cache reuse is the primary lever. We explain why KV-aware

routing through a fleet-wide scheduler is the first optimization to apply, ahead of adding capacity.

Next, prefill/decode disaggregation. We separate compute-bound prefill from memory-bound decode, and

examine what public benchmarks omit: the conditions under which P/D disaggregation shines, and the

workload shapes that justify the added architectural complexity. We close with GLM-5.2 and show

the equivalent stack assembled in the open: cache-aware routing, P/D disaggregation, tiered KV

offload, and wide expert parallelism — implemented on vLLM and llm-d. Attendees leave with a tuning

decision framework: which lever to apply first, how to read workload signals, and where additional

GPUs do and don't help.

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