Hello everyone! 👋 I'm excited to share what we have in plan for Q3 2025 for Ray. I will try to keep this updated as features get merged in, and rolled out.

Goal: Deliver foundational reliability, performance, and DX improvements across Ray Core, Data, Train, LLM, Serve, RL, Observability, Technical Content, and KubeRay.

Ray Core

Reliability & Fault Tolerance

• Improve system stability under node and network failures,
  • Including making RPCs tolerant to transient errors
• Add robust support for preemptible instances

Scheduling & Performance

• Introduce label-based scheduling for finer-grained resource control ([Core] Ray Label Selector API Implementation Tracker #51564)
• Implement GPU objects with RDMA transfer support for high-performance GPU data handling ([Core] verl gpu tensor rdma integration #54943)

Developer Experience

• Introduce ActorMesh for simplified interaction with groups of actors ([WIP] [Prototype] ActorMesh API #54760)
• Improve static typing across the codebase to enhance developer productivity
• Address outstanding technical debt in core worker components

Ecosystem Integrations

• Provide official support for reinforcement learning libraries like veRL, OpenRLHF, and ROLL ([RFC] Improving Ray for Post-Training / RL for LLM Projects #54021)

Ray Data

Reliability

• Ensure workloads complete successfully despite cluster failures

Performance

• Enhance training ingest pipelines with advanced sampling and caching support

Connectors

• Improve Apache Iceberg integration
• Expand data catalog support, starting with Databricks Unity Catalog

Usability

• Schema UDFs
• Enhanced internal query planning

Ray Train

API

• Finalize Train v2 API

Performance

• Implement asynchronous checkpointing

LLM

Goal: Run large models (e.g. DeepSeek) at scale via vLLM on Ray Serve:

• Prefill diaggregation
• Large scale DP
• Custom request routing
• Elastic expert parallelism

Performance & Efficiency

• Implement prefill disaggregation to optimize performance for large context models.
• Develop an intelligent, KV cache-aware router with a pluggable architecture
• Implement Distributed Parallel (DP) Attention within Ray Serve

Operations

• Publish updated performance benchmarks

Ecosystem

• Support SkyRL for reinforcement learning for human feedback (RLHF) workloads

Ray Serve

Serving Flexibility

• Custom auto‑scaling and routing patterns
• Async inference support
• MCP server patterns
• Integrate label based scheduling

Observability

• Enhanced tracing support

RLlib

• Ray RL V2 stack GA
• Algorithm composability enhancements

Observability

API Release

• Public launch of unified event export API

Optimization

• Refactor internals to leverage new export API

Technical Content

• New technical templates
• More examples & deep‑dives

KubeRay

Upgrades

• Productionize the incremental upgrade feature for seamless cluster updates

Hardware Support

• Streamline support for diverse accelerators, including multiple GPU types, Dynamic Resource Allocation (DRA), and MIG

Autoscaling

• Continue to improve the functionality and reliability of Autoscaler V2

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We love hearing from the community! If there is a feature you'd like to see in Ray in the future, let us know by filing a feature request or comment here. Thank you for supporting Ray!