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Ray 2.59 Graduates Ray Data LLM and Ray Serve LLM to General Availability and Enables Token Authentication by Default for Local Clusters

Ray 2.59.0 declares its LLM batch and serving APIs generally available, upgrades to vLLM 0.27.0, and turns on token authentication for local clusters, with all clusters to follow in 2.61.

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Overview

The Ray distributed-computing project has declared its large-language-model batch and serving libraries generally available. According to the Ray 2.59.0 release notes, Ray Data LLM and Ray Serve LLM “graduate to general availability this release” in the same release that upgrades the bundled vLLM engine to version 0.27.0. The release, published on GitHub on October 2, also changes a security default: Ray now turns on token authentication for local clusters.

What Changed for LLM Workloads

The release notes list Ray Data LLM and Ray Serve LLM as GA, which the project also labels stable, and pair the change with the vLLM 0.27.0 upgrade. Other changes under the LLM heading are smaller: heavy engine imports are now deferred from ray.data.llm, multipart/form-data file uploads are supported in HttpRequestUDF, engine errors are surfaced on the direct-streaming ASGI app, and a bug that dropped s3:// model sources for streaming load formats was fixed, according to the same notes.

The release also adds documentation for two serving topics. One is a KV-aware routing guide covering installation, configuration, scoring and tuning; the other is a guide for serving LLMs on TPUs, per the 2.59.0 notes. The routing feature itself arrived earlier: the Ray 2.58.0 notes, published August 23, say that release completed KV cache and token aware request routing, which had been previewed in 2.57. In that design, tokenization happens in-process on the LLMRouter ingress replica and the routing decision is made there.

Token Authentication Becomes the Default, Locally

The security change is the one most likely to affect existing users. Per the 2.59.0 notes, ray.init() without an address now enables authentication and generates a token at ~/.ray/auth_token when none exists. The ray start --head command enables authentication when a token is already available, and otherwise warns that it started an unauthenticated cluster. Setting RAY_AUTH_MODE=disabled opts out. Remote and multi-node clusters are unchanged in this release.

That exemption is temporary. The notes say that in Ray 2.61, token authentication becomes the default for all clusters, including remote and multi-node ones, and that every node in a cluster and every client that connects to it will need the same token. The project tells operators of remote clusters to set up token distribution before upgrading, according to the release notes.

Two related hardening items are listed. The dashboard now redacts runtime_env values in browser-facing endpoints, which the notes say routinely carry credentials, and read_hudi gained an unpickling guard that prevents remote code execution.

Serve and Data Additions

For Ray Serve, the release notes describe three new features:

  • A per-deployment BackpressureConfig that returns HTTP 429 instead of 503 when a request is rejected for exceeding max_queued_requests, with an optional Retry-After header. Defaults are unchanged.
  • A declarative TracingConfig model with enabled, exporter_import_path and sampling_ratio fields, passed to serve.start(tracing_config=...) or set in a Serve config file. Setup errors now fail fast instead of being silently swallowed.
  • Scale-to-zero for gang-scheduled deployments, which can now set min_replicas=0.

On the data side, the same notes describe an external, disk-backed mode for the shuffle_v2 backend, with join and aggregation support, native ORC file reading, and multi-path support in read_lance. The shuffle strategy was renamed from hash_shuffle_v2 to shuffle_v2, and the compression setting from hash_shuffle_compression to shuffle_compression, so users of the earlier names will need to update configuration.

In Ray Core, the notes say the release removed a GCS resource-view reset that caused placement-group retry storms on large clusters, and added support for Mobilint accelerators.

Housekeeping

The notes also state that Ray 2.59.0 is the last release that will publish CUDA 11.7 (-cu117) images, which matters for teams still building on those base images.

What We Don’t Know

  • The release notes do not define the criteria behind the GA designation or describe any compatibility guarantees attached to it.
  • Ray 2.59.0 pins vLLM 0.27.0, while The Machine Herald previously reported on vLLM 0.31; the notes do not say how Ray plans to track later vLLM releases.
  • The notes do not say when Ray 2.61 will ship.

Analysis

The release pairs a GA label for its LLM libraries with a default-on security setting. The staged approach to authentication, local clusters first and all clusters in 2.61, gives operators of remote clusters advance notice to distribute tokens, which the project itself says they should do before upgrading.