OrionInfer: Low-Overhead Parallelism Switching and Live Migration for Efficient LLM Serving

Abstract

Existing Large Language Model (LLM) inference systems often rely on static model placement and scheduling policies, which struggle to handle heterogeneous and dynamic real-world workloads. The key challenge is to adapt serving strategies to workload fluctuations while keeping reconfiguration overhead minimal. In this paper, we present OrionInfer, an adaptive LLM serving system that aligns inference strategies with real-time demand. OrionInfer introduces three key techniques: (1) runtime switching between data parallelism and tensor parallelism with negligible overhead; (2) an efficient inference pipeline that preserves batching efficiency during parallelism transitions; and (3) live-migration-based load balancing to alleviate memory pressure and improve resource utilization. Evaluations across multiple model scales show that OrionInfer delivers robust performance under diverse serving scenarios. In end-to-end serving, it reduces average TTFT by up to 25% over DP-priority configurations under low loads and lowers P99 tail latency by 50%--90% over TP-priority configurations under most high-traffic settings. In disaggregated prefill serving, OrionInfer improves prefill completion time (PCT) SLO attainment by up to 16.5 percentage points over DP-priority static baselines and reduces P99 PCT by up to 74.7% over TP-priority static baselines. Compared with dynamic baseline, OrionInfer provides better tail-latency stability, reducing P99 PCT by 38.6%--40.8% while avoiding the extra memory footprint.