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UID:pretalx-devconf-us-2025-XZ9HUJ@pretalx.devconf.info
DTSTART;TZID=EST:20250920T110000
DTEND;TZID=EST:20250920T113500
DESCRIPTION:Effectively deploying Large Language Models (LLMs) in Kubernete
 s is critical for modern AI workloads\, and vLLM has emerged as a leading 
 open-source project for LLM inference serving. This session will explore t
 he unique features of vLLM\, which set it apart by maximizing throughput a
 nd minimizing resource usage. We’ll explore the lifecycle of deploying A
 I/LLM workloads on Kubernetes\, focusing on achieving seamless containeriz
 ation\, efficient scaling with Kubernetes-native tools\, and robust monito
 ring to ensure reliable operations. \n\nBy simplifying complex workloads a
 nd optimizing performance\, vLLM drives innovation in scalable and efficie
 nt LLM deployment by leveraging features like dynamic batching and distrib
 uted serving\, making advanced inference accessible for diverse and demand
 ing use cases. Join us to learn why vLLM is shaping the future of LLM serv
 ing and how it integrates into Kubernetes to deliver reliable\, cost-effec
 tive\, and high-performance AI systems.
DTSTAMP:20260315T085337Z
LOCATION:101 (Capacity 48)
SUMMARY:Cloud-Native Model Serving: vLLM's Lifecycle in Kubernetes - Cedric
  Clyburn\, Roberto Carratalá
URL:https://pretalx.devconf.info/devconf-us-2025/talk/XZ9HUJ/
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