Ray Summit 2026: Physical AI, RL, and the infrastructure that runs them all

Ray Summit 2026 brought more than 2,000 attendees to San Francisco to examine how reinforcement learning, agentic systems, physical AI, and large-scale inference have become production infrastructure challenges requiring resilient coordination across heterogeneous CPU, GPU, and TPU resources. Speakers from organizations including Lila Sciences, Torc Robotics, NVIDIA, Bedrock Robotics, Periodic Labs, Microsoft AI, Recursion, Spotify, and Capital One described Ray-based systems for scientific experimentation, autonomous trucking and construction equipment, open-model post-training, drug discovery, financial modeling, and high-volume ML platforms, often reporting improvements in data throughput, GPU utilization, training speed, and operational scale. Key announcements and technical themes included expanded TPU support in Ray, topology-aware distributed training, Microsoft’s RELAY proxy for large Ray deployments, vLLM’s evolving open inference roadmap, and a new Agentic API layer for managing stateful multi-turn model interactions. The event also hosted its largest training program to date, serving more than 800 participants through hundreds of concurrent cloud workspaces and thousands of GPUs.

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