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See how Ray Serve LLM updates in Ray 2.55 enable DP group fault tolerance for vLLM Wide Expert Parallelism (WideEP)

DP Group Fault Tolerance for vLLM

Announcing DP Group Fault Tolerance for vLLM WideEP Deployments with Ray Serve LLM

Richard Liaw

Richard Liaw headshot

Ray Data LLM enables scalable batch LLM inference with vLLM, delivering high throughput, fault tolerance, and 2x performance for large workloads.

Ray Data LLM enables 2x throughput over vLLM’s synchronous LLM engine at production-scale

Ray has become the engine for AI-native computing for thousands of AI- and digital-natives. Now Anyscale and Microsoft are partnering to bring the power of Ray to every enterprise.

Announcing first-party service - Anyscale on Azure

Announcing Anyscale on Azure: Build, Run and Scale AI-Native Workloads Securely on Azure Infrastructure

Ray, the open-source distributed compute framework for AI is joining the PyTorch Foundation, part of The Linux Foundation. 

Ray joins PyTorch Foundation

Ray is Joining The PyTorch Foundation

opensource stack blog thumbnail

Components of an Open Source AI Compute Tech Stack

An Open Source Stack for AI Compute: Kubernetes + Ray + PyTorch + vLLM

Explore a technical comparison of leading Reinforcement Learning (RL) libraries for LLMs from Ray. This guide analyzes frameworks like TRL, Verl, and RAGEN to help developers choose the best tools for RLHF, reasoning, and agentic AI.

The architecture of a Reinforcement Learning (RL) library is split into two primary components: Generation and Training. During the generation phase, an LLM Engine performs multi-turn rollouts within an environment to produce data and reward signals. This output is then fed into the training phase to update the model's parameters. This process forms a feedback loop, where the progressively improved model generates the next iteration of data for continuous refinement.

Open Source RL Libraries for LLMs Graph

Open Source RL Libraries for LLMs

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Anyscale is the leading AI application platform. With Anyscale, developers can build, run and scale AI applications instantly.

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