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原文摘要

Agent workflows issue dependent model calls that share long context prefixes, so serving them efficiently depends on reusing the key-value (KV) cache of those prefixes. On accelerators that compile model graphs ahead of time, however, the device KV budget is fixed, and prefixes evicted under load must be recomputed. Restoring them from host memory avoids this recomputation, but the transfer occupies the same engine thread as prefill and decode, so its benefit is not guaranteed. In this paper, we study when restoring beats recomputing on a commercial static-graph neural processing unit (NPU) stack (Rebellions ATOM+, vllm-rbln 0.10.2) that serves a Qwen3-8B planner on two cards and a Qwen3-0.6B executor on one. We first show that repeated prefill dominates planner work when default serving collapses: 89.5% of the executed planner prompt tokens had been materialized before. We then build a synchronous host-DRAM tier on the runtime's public KV copy calls, together with a scheduler-aware event model whose predictions are frozen before each test cohort. Experimental results show that the host tier raises workflow service-level objective (SLO) attainment from 22.8% to 100% and from 9.4% to 99.8% in the two tested overload conditions, and endpoint confirmation on two new seeds supports conservative load gains of 11.8% (chain) and 18.2% (fork), while mixed and bursty workloads remain seed-sensitive. The frozen model predicts median and 99th-percentile (P99) latency within its 15% tolerance in all 24 common-load conditions, including the 16-18% P99 increase that the tier causes for fork workflows, but it misorders three policy contrasts and overestimates the benefit of a cheaper store path (11% predicted against 3.1% measured). The trade-off therefore depends on runtime scheduling and transfer cost as well as cache capacity.