vLLM Production Stack provides a reference implementation on how to build an inference stack on top of vLLM, allowing for scalable, monitored, and performant LLM deployments using Kubernetes and Helm.
This article dives into designing a scalable distributed job scheduling service that can handle millions of tasks. It covers system components, API design, scaling strategies, handling failures, and addressing single points of failure.
High-performance deployment of the vLLM serving engine, optimized for serving large language models at scale.
