Breaking the Ice: Analyzing Cold Start Latency in vLLM
As scalable inference services become popular, the cold start latency of an inference engine becomes important. Today, vLLM has evolved into the de-facto inference engine of choice for many inference workloads. Although popular, due to its complexity and rapid evolution, there has not been a systematic study on the startup latency of its engine. With major architectural innovations under it (e.g., the V1 API, introduction of torch.compile), in this paper, we present the first detailed performance characterization of vLLM startup latency. We break down the startup process into six foundational steps and demonstrate that this process is predominantly CPU-bound. Each step exhibits consistent...
| Authors | Huzaifa Shaaban Kabakibo, Animesh Trivedi, Lin Wang |
|---|---|
| Primary category | cs.LG |
| Published | 5 June 2026 |
| Last updated at source | 29 June 2026 |
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