Ray

What Is Ray?

Ray is an open-source distributed computing framework originally developed at UC Berkeley's RISELab and now maintained by Anyscale. It provides a universal compute layer that enables developers to scale Python and AI applications from a single laptop to clusters of thousands of GPUs without requiring deep expertise in distributed systems. Ray has become the de facto infrastructure backbone for training, fine-tuning, and serving large language models, with over 237 million downloads and adoption by organizations including OpenAI, which uses Ray to coordinate the training of ChatGPT.

Core Architecture and Libraries

At its foundation, Ray consists of a core distributed runtime that handles task scheduling, object management, and fault tolerance across heterogeneous compute clusters. Built on top of this runtime is a suite of domain-specific AI libraries: Ray Train for distributed model training across frameworks like PyTorch and TensorFlow; Ray Data for scalable data preprocessing and ingestion pipelines; Ray Tune for hyperparameter optimization and experiment management; Ray Serve for online model inference and serving; and RLlib, an industry-grade reinforcement learning library used in gaming, robotics, autonomous vehicles, and industrial control. This modular architecture allows teams to compose end-to-end ML workflows that span data processing, training, and deployment within a single unified platform.

Ray and the Agentic Economy

Ray has become essential infrastructure for the emerging agentic economy. As autonomous AI agents grow more sophisticated—planning multi-step tasks, invoking external tools, and coordinating with other agents—they demand compute frameworks capable of massively parallel execution with low latency. Anyscale's platform, built on Ray, now supports building agentic applications with Model Context Protocol (MCP) integration, enabling organizations to deploy complex multi-agent systems at scale. Nearly every major open-source reinforcement learning framework for post-training LLMs is built on top of Ray, reflecting its central role in developing the reasoning and planning capabilities that underpin agentic behavior.

Industry Adoption and Ecosystem

In 2025, Ray joined the PyTorch Foundation under the Linux Foundation, completing a critical layer in the open-source AI stack alongside PyTorch and vLLM. Major cloud providers have integrated Ray deeply into their platforms: Google Cloud offers Ray on Vertex AI, Microsoft Azure provides managed Ray through Anyscale on AKS, and AWS supports Ray through SageMaker. This broad ecosystem support, combined with partnerships with NVIDIA for optimized GPU utilization, has made Ray the standard compute engine for organizations building production AI systems—from generative AI foundation models to real-time recommendation systems to massively parallel agentic simulations.

Gaming, Simulation, and Beyond

RLlib's reinforcement learning capabilities make Ray particularly relevant to gaming and simulation workloads. Game studios and researchers use RLlib for training intelligent NPCs, optimizing game balance through simulation, and running multi-agent environments like StarCraft II at scale. Beyond gaming, Ray powers use cases in autonomous driving simulation, financial backtesting, climate modeling, and logistics optimization. As simulating reality becomes increasingly central to AI development—both for training agents and for testing them before real-world deployment—Ray's ability to orchestrate thousands of parallel simulation instances positions it as foundational infrastructure for the convergence of AI, gaming, and spatial computing.