Study Characterizes Cold Start Latency in vLLM Inference Engine
Researchers published the first systematic analysis of vLLM's startup latency, breaking down the initialization process into six steps and identifying it as predominantly CPU-bound. vLLM has become the standard inference engine for large language model deployments, but its startup performance had not been previously studied in detail. The findings enable better resource planning for large-scale inference services and include an analytical model for predicting startup latency.
A new preprint on arXiv presents the first detailed performance characterization of vLLM startup latency, addressing a gap in understanding this widely-used inference engine. The researchers decomposed the startup process into six foundational steps and demonstrated that initialization is predominantly CPU-bound rather than memory or I/O constrained. They identified consistent and interpretable scaling trends across model-level and system-level parameters, enabling fine-grained attribution of latency sources. Building on these insights, the team developed a lightweight analytical model capable of accurately predicting vLLM startup latency for given hardware configurations. The study accounts for recent architectural innovations including the V1 API and torch.compile integration. All benchmarking datasets, analysis tools, and prediction scripts have been open-sourced to support reproducibility and practical application.
What different sources said
- arXiv cs.LGCenter
Breaking the Ice: Analyzing Cold Start Latency in vLLM
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