Ray Summit 2026: where the community meets production

Ray Summit 2026 wrapped in late August after three days that put the Ray and vLLM open-source communities on the same stage for the first time. Robert Nishihara, Anyscale co-founder, opened the main event before hundreds of engineers who build training and inference systems at real scale — not laptop demos, but data pipelines running under production load.

The speaker roster was built from teams that have already paid the tuition in blood. Discord walked through its scaling journey with Ray and Anyscale. Spotify broke down the Hendrix framework for training LLMs. Apple Maps showed how it runs batch inference and model evaluation at global-map scale. Recursion brought a multimodal angle with a virtual biology platform running on hybrid infrastructure.

Several research threads pushed boundaries. Researchers presented an open recipe for training knowledge agents on 397 billion tokens under SkyRL, diffusion models that redefine token efficiency, and a tabular foundation model for payments built on one trillion tokens. The question "why current AI doesn't really see the world" got its own slot — a sign the community isn't satisfied with standard benchmarks.

The pre-conference training day wasn't aimed at beginners. Full-day sessions covered architectures, performance optimizations, and the operational patterns behind modern production AI systems: large-scale data pipelines, distributed foundation-model training, and high-performance inference platforms. The engineers who maintain Ray and vLLM day to day led the workshops themselves.

Industry panels covered physical AI and life sciences. The Ray party closed day two in the atmosphere that reminds you why this community endures. Every ticket includes post-event recording access, and a five-ticket bundle is discounted for teams that want to attend together next year.