Crossflow: Prefill-Decode Elasticity for Agentic LLM Serving
As serving capacity demand surpasses that of training, serving efficiency becomes increasingly important. Prefill-decode (P/D) disaggregation improves serving efficiency through specialization and isolation of the two phases. These benefits rest on a static partitioning. Phase demand, however, is not static. We observe that in a large LLM fleet the ratio of uncached input to output tokens has peak-to-mean ratios up to 4.7x at minute timescales, and that in a public agentic trace the hourly ratio spans a median 24.5x within a single day, while reassigning a replica takes tens of minutes. Agentic traffic sharpens the mismatch. Sizing each pool at its ninety-fifth percentile leaves up to 17%...
| Authors | Yi Xu, Ehsan K. Ardestani, Wenyin Fu et al. |
|---|---|
| Primary category | cs.DC |
| Published | 22 September 2026 |
| Last updated at source | 22 September 2026 |
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