Study Characterizes Cold Start Latency in vLLM Inference Engine
Researchers have published the first detailed performance characterization of startup (cold start) latency in vLLM, the widely used open-source LLM inference engine. The study breaks the startup process into six distinct steps, finding it is predominantly CPU-bound, and develops a lightweight analytical model to predict latency based on hardware configuration. The findings offer actionable guidance for resource planning in large-scale AI inference deployments.
A paper accepted to the 9th MLSys Conference (2026) presents the first systematic study of cold start latency in vLLM, which has become the de facto inference engine for many large language model serving workloads. The researchers decompose the vLLM startup process into six foundational steps and characterize how each scales with model-level and system-level parameters. Their analysis finds that startup latency is predominantly CPU-bound, with each step exhibiting consistent and interpretable scaling trends that allow fine-grained attribution of latency sources. Leveraging these insights, the team builds a lightweight analytical model capable of accurately predicting vLLM startup latency for a given hardware configuration. The study also accounts for recent major architectural changes in vLLM, including the V1 API. All benchmarking datasets, analysis tools, and prediction scripts have been open-sourced to facilitate reproducibility and further research.
What's missing
It is unclear how startup latency compares to competing inference engines (e.g., TensorRT-LLM, TGI), which would contextualize the practical significance of the findings.
What different sources said
- arXiv cs.LGCenter
Breaking the Ice: Analyzing Cold Start Latency in vLLM
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