Résultats du processus de synthèse de l'article arXiv : 2504.09285v2

Résumé : LLM inference must meet strict latency SLOs (e.g., 100 ms P99 time-between-tokens) while maximizing goodput. Yet, real-world variability in prompt and response lengths skews compute-intensive prefill and memory-bound decode phases, making both colocated (even with chunked prefill) and disaggregated deployments unable to simultaneously deliver low tail latency and high throughput. We introduce DynaServe, a high-performance LLM serving system built atop vLLM that unifies and extends both paradigms for maximizing goodput under SLO constraints, when handling unbalanced and dynamic workloads. It relies on a micro-request abstraction, which arbitrarily splits each request at any token boundary into at most two cooperating segments. A two-level scheduling framework then balances micro-request load across unified GPU instances. The global scheduler rapidly selects per-request split points by considering both the request's prefill/decode time ratio and the current load across GPU instances. The local schedulers on each GPU instance independently form SLO-aware batches, adjusting their composition in response to workload fluctuations, potential latency spikes and per-GPU under/over utilization. On real-world traces, DynaServe boosts the overall serving capacity from 1.15$\times$ to 3.07$\times$, improves goodput by up to 1.91$\times$ and 1.61$\times$, and improves the performance by up to 60\% in a hybrid workload under SLO compared to state-of-the-art colocated and disaggregated baselines.