SGLang Office Hours - Scaling LLM Serving with Ray and SGLang

​📣 Scaling LLM Serving with Ray and SGLang

​SGLang Office Hours is back!

​We're co-hosting with Anyscale to dig into how Ray powers large-scale LLM serving with SGLang.

​Xinyu Zhang (@xinyzng), MTS at Anyscale, opens with a deep dive into the Ray executor backend inside SGLang: why Ray is needed, how it improves RL workload placement, and what you get from Ray cluster integration.

​Jeffrey Wang (@jeffreyycwang), SWE at Anyscale, covers how Ray Serve fits into large-scale SGLang deployments, the roadmap, and where the community can contribute. We'll close with a live demo and walkthrough of features open for contribution.

​Whether you're serving at scale or just getting started with the Ray integration, come with your questions.
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