Bullet: Boosting GPU Utilization for LLM Serving via Dynamic Spatial-Temporal Orchestration

Modern large language model (LLM) serving systems confront inefficient GPU utilization due to the fundamental mismatch between compute-intensive prefill phase and memory-bound decode phase. While current practices attempt to address this by organizing these phases into hybrid batches, such solutions create an inefficient tradeoff that sacrifices either throughput or latency, leaving substantial GPU resources underutilized. For this, we identify two key root causes: 1) the prefill phase suffers from suboptimal compute utilization due to wave quantization and attention bottlenecks, and 2) hybrid batching disproportionately prioritizes latency over throughput, wasting both compute resources and memory bandwidth. To mitigate the issues, we present Bullet, a novel spatial-temporal orchestration system that eliminates these inefficiencies through fine-grained phase coordination. Bullet enables concurrent execution of prefill and decode requests, while dynamically provisioning GPU resources based on real-time performance modeling. By integrating SLO-aware scheduling and adaptive resource allocation, Bullet maximizes GPU utilization without compromising latency targets. Experimental evaluations on real-world workloads demonstrate that Bullet delivers 1.26× average throughput gains (up to 1.55×) over state-of-the-arts, while consistently meeting latency constraints.