The growing demand for large language models (LLMs) requires serving systems to handle many concurrent requests with diverse service level objectives (SLOs). This exacerbates head-of-line (HoL) blocking during the compute-intensive prefill phase, where long-running requests monopolize resources and delay higher-priority ones, leading to widespread time-to-first-token (TTFT) SLO violations. While chunked prefill enables interruptibility, it introduces an inherent trade-off between responsiveness and throughput: reducing chunk size improves response latency but degrades computational efficiency, whereas increasing chunk size maximizes throughput but exacerbates blocking. This necessitates an adaptive preemption mechanism. However, dynamically balancing execution granularity against scheduling overheads remains a key challenge. In this paper, we propose FlowPrefill, a TTFT-goodput-optimized serving system that resolves this conflict by decoupling preemption granularity from scheduling frequency. To achieve adaptive prefill scheduling, FlowPrefill introduces two key innovations: 1) Operator-Level Preemption, which leverages operator boundaries to enable fine-grained execution interruption without the efficiency loss associated with fixed small chunking; and 2) Event-Driven Scheduling, which triggers scheduling decisions only upon request arrival or completion events, thereby supporting efficient preemption responsiveness while minimizing control-plane overhead. Evaluation on real-world production traces shows that FlowPrefill improves maximum goodput by up to 5.6× compared to state-of-the-art systems while satisfying heterogeneous SLOs.
Figures & tables
Figure 1. Request length distributions and arrival patterns for different task types in QwenTrace ( Wang et al., 2025 ) . We explore the possibility of scheduling requests with diverse SLO guarantees.
Figure 2. Using different prefill granularities to meet the SLO of time-to-first-token.
Figure 3. Throughput and latency of chunked prefill for a 32K-token input under different chunk sizes when serving an Llama3-8B. Alternating colors within each bar demarcate individual chunks.
Figure 4. Throughput and normalized TTFT during the prefill phase for different input lengths when serving an Llama3-8B.
Figure 5. Overview of FlowPrefill.
Figure 6. Execution logic is hierarchical, and preemption checks can be placed at different execution boundaries. Preemption checks at the chunk or layer level result in coarser preemption granularity, whereas FlowPrefill places them at core operator boundaries for the finest-grained preemption. (Blue circles denote preemption checks.)
Figure 7. Cooperative preemption process.
Figure 8. Example of serving two requests with different priorities in FlowPrefill.
Model
Text
Image
Search
File
Llama3-8B
0.25s
0.5s
4.0s
6.0s
Qwen2.5-14B
0.4s
0.8s
6.5s
9.0s
Llama3-70B
1.0s
2.0s
15.0s
18.0s
Table 2. Settings of different TTFT SLO requirements.
Figure 9. End-to-end performance of three models under varying request rates and SLO requirements on QwenTrace.
Figure 10. Performance comparison under three scheduling policies.
Figure 11. Performance comparison under varying batch token budgets and no batching.
Figure 12. Average preemption blocking time under operator- and layer-level preemption boundaries. Different preemption granularities are realized by placing preemption checks at different execution boundaries.
Figure 13. Real and predicted TTFT for three models serving a trace segment from QwenTrace at a given request rate.
Figure 14. Performance comparison under a single-SLO workload. FlowPrefill matches baseline throughput, indicating negligible operator-level preemption (§ 5.1 ) overhead but delivers better SLO attainment as request rates scale.
Figure 15. Performance of FlowPrefill with chunked prefill under different chunk sizes.
Figure 16. Performance comparison under the PD-colocation setting across varying request rates, reporting TTFT and TBT SLO attainment.
Figure 17. Performance comparison for serving Qwen3-30B-A3B.
School of Computer Science and Technology, Beijing Institute of Technology · School of Computer Science and Engineering, Sun Yat-sen University · School of Computer Engineering, Jiangsu Ocean University