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ray-llm

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LLM workloads on open-source Ray (pinned to 2.57) — OpenAI-compatible serving with ray.serve.llm (vLLM-backed LLMConfig + build_openai_app) and batch inference with ray.data.llm (build_processor). Corrects the stale defaults a model produces — the archived ray-llm repo and its YAML configs, hand-rolled vLLM engines inside plain Serve deployments, the removed build_llm_processor name, deprecated boolean stage flags, top-level LLMServer/LLMRouter imports, free-form accelerator strings, one-deployment-per-LoRA-adapter designs — with the 2.57 idioms (stage configs, placement_group_config over hand-rolled PGs, deployment_config autoscaling, dynamic LoRA multiplexing, prefix-cache-affinity routing, the full OpenAI endpoint surface). Use when writing, reviewing, or productionizing LLM serving or batch inference on Ray. Classic-ML Ray (Train/Tune/Data/Serve/clusters) lives in the sibling ray skill.

skills/.experimental/ray-llm/SKILL.md

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# Ray LLM

Library-reference skill for LLM workloads on open-source Ray — 13 rules across 5 categories covering `ray.serve.llm` (OpenAI-compatible, vLLM-backed serving) and `ray.data.llm` (batch inference). This surface churned faster than any other part of Ray — a standalone repo was absorbed and archived, entry points were renamed, and config shapes restructured — so the examples a model learned from mostly no longer run. Each rule names the wrong default it corrects; there is no rule for things a capable model already gets right.

Scope is the LLM-specific layer. Generic Serve/Data/cluster decisions (deployment lifecycle, autoscaling semantics, KubeRay) are the sibling [`ray`](../ray/) skill — the two compose.

Pinned to **ray 2.57.0** (`ray[llm]` extra, which pins its matching vLLM). API claims were verified against the unpacked 2.57.0 wheel and the installed package source, and every config example in the rules was constructed under CPU-only pydantic validation (including the traps, which fail exactly as described); engine/GPU runtime behavior is source-verified only — no model was actually served.

## When to Apply

- Standing up or reviewing an OpenAI-compatible LLM serving deployment on Ray
- Writing batch LLM inference over datasets — summarization, embedding, scoring at scale
- Sizing or placing multi-GPU models — tensor/pipeline parallelism, accelerator selection
- Scaling LLM deployments — replica autoscaling, ingress sizing, request routing