Dynamic Pipeline Inference Optimization for LLMs: Load-Aware Partitioning, Updating, and Migration

Abstract

With the rapid advancement of deep learning technology, the parameter scale of large language models has grown exponentially, expanding from hundreds of millions in the early stages to hundreds of billions or even trillions today. Pipeline inference is a crucial approach enabling efficient inference in large language models. However, existing pipeline inference relies on static layer allocation, ignoring the intrinsic variance in layer-wise computation and memory footprints, as well as runtime fluctuations in request rates and sequence lengths. Consequently, under dynamic workloads, compute-dense stages rapidly bottleneck the pipeline and induce severe queue blocking while leaving other devices idle, ultimately degrading end-to-end latency and severe GPU underutilization. To address these challenges, this paper proposes a dynamic pipeline parallel inference algorithm. Centering on the three phases of LLM pipeline inference—partitioning, updating, and migration—the algorithm introduces: (1) A proactive update trigger mechanism driven by multidimensional load forecasting. Rather than relying on reactive bottleneck indicators, it translates projected request rates and token lengths into stage-level VRAM demands, preemptively initiating reconfiguration only when impending hardware capacity violations are detected; (2) A joint partition-migration optimization strategy utilizing a two-stage biased random key genetic algorithm. By embedding a maximum-weight bipartite matching formulation into the evolutionary fitness evaluation, this strategy mathematically couples pipeline boundary search with physical state mapping, maximizing resident parameter reuse to guarantee minimal-overhead model migration; (3) Distributed cluster experiments conducted using public datasets and the Ray framework demonstrate that the proposed method outperforms existing state-of-the-art pipeline inference solutions in metrics including response latency and resource overhead, specifically improving throughput by 2.3% compared to the SOTA framework.