Uploaded November 2025 | Updated September 2026, 1 week ago
At Ray Summit 2025, Ying Sheng from SGLang and Qiaolin Yu from Anyscale share how SGLang has become a high-performance framework for LLM serving—powering production workloads at leading companies through its optimized architecture and next-generation inference capabilities.
They begin by breaking down SGLang’s core features, including its lightweight execution engine, optimized KVCache handling, and flexible serving abstractions designed for both high-throughput batch workloads and ultra–low-latency interactive use cases.
The speakers then dive into the key performance optimization techniques that enable SGLang to consistently outperform traditional serving stacks. Topics include scheduling strategies, memory management improvements, parallel execution paths, and advanced kernel optimizations tailored for modern accelerators.
Ying and Qiaolin also share insights from real-world production deployments, highlighting how companies use SGLang to support rapid model iteration, cost-efficient scaling, and stable high-volume traffic patterns.
Finally, they present the future roadmap for SGLang—covering upcoming performance enhancements, deeper hardware integration, and new features aimed at simplifying large-scale LLM serving for both enterprises and open-source users.
Attendees will leave with a deep understanding of how SGLang achieves best-in-class performance and where the framework is headed next.
Liked this video? Check out other Ray Summit breakout session recordings youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh
Subscribe to our YouTube channel to stay up-to-date on the future of AI! youtube.com/c/anyscale
🔗 Connect with us:
LinkedIn: linkedin.com/company/joinanyscale
X: https://x.com/anyscalecompute
Website: anyscale.com
At Ray Summit 2025, Ying Sheng from SGLang and Qiaolin Yu from Anyscale share how SGLang has become a high-performance framework for LLM serving—powering production workloads at leading companies through its optimized architecture and next-generation inference capabilities.
They begin by breaking down SGLang’s core features, including its lightweight execution engine, optimized KVCache handling, and flexible serving abstractions designed for both high-throughput batch workloads and ultra–low-latency interactive use cases.
The speakers then dive into the key performance optimization techniques that enable SGLang to consistently outperform traditional serving stacks. Topics include scheduling strategies, memory management improvements, parallel execution paths, and advanced kernel optimizations tailored for modern accelerators.
Ying and Qiaolin also share insights from real-world production deployments, highlighting how companies use SGLang to support rapid model iteration, cost-efficient scaling, and stable high-volume traffic patterns.
Finally, they present the future roadmap for SGLang—covering upcoming performance enhancements, deeper hardware integration, and new features aimed at simplifying large-scale LLM serving for both enterprises and open-source users.
Attendees will leave with a deep understanding of how SGLang achieves best-in-class performance and where the framework is headed next.
Liked this video? Check out other Ray Summit breakout session recordings youtube.com/playlist?list=PLzTswPQNepXllnU0C36WtkC0dqkAoDulh
Subscribe to our YouTube channel to stay up-to-date on the future of AI! youtube.com/c/anyscale
🔗 Connect with us:
LinkedIn: linkedin.com/company/joinanyscale
X: https://x.com/anyscalecompute
Website: anyscale.com
