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Jeffrey Wang

Kourosh Hakhamaneshi

Abrar Sheikh

Seiji Eicher

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

Alex Yang

Akshay Malik

Spencer Peterson (Google)

Ray Serve upgrades deliver faster online inference with HAProxy and gRPC, enabling scalable LLM inference on Ray with lower latency and higher throughput.

Major upgrades to Ray Serve: Online Inference with 88% lower latency and 11.1x higher throughput

Accelerate service debugging with new Ray Serve Grafana dashboard on Anyscale. Available with Ray v2.54+

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Ray Serve’s New Grafana Dashboard: Debugging That Wasn’t Possible Before

Harshit Agarwal

Blog header - Ray Serve custom routing and more

Ray Serve: Advancing Flexibility with Async Inference, Custom Request Routing, and Custom Autoscaling

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

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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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