[None][doc] Ray orchestrator initial doc (#8373)

Signed-off-by: Erin Ho <14718778+hchings@users.noreply.github.com>

docs/source/features/ray-orchestrator.md

@@ -0,0 +1,42 @@
+# Ray Orchestrator (Prototype)
+
+```{note}
+This project is under active development and currently in a prototype stage. The current focus is on core functionality, with performance optimization coming soon. While we strive for correctness, there are currently no guarantees regarding functionality, stability, or reliability.
+```
+
+## Motivation
+The **Ray orchestrator** uses [Ray](https://docs.ray.io/en/latest/index.html) instead of MPI to manage workers for single- and multi-node inference. It’s a first step toward making TensorRT-LLM a better fit for Reinforcement Learning from Human Feedback (RLHF) workflows. For RLHF, Ray can dynamically spawn and reconnect distributed inference actors, each with its own parallelism strategy. This feature is a prototype and under active development. MPI remains the default in TensorRT-LLM.
+
+
+## Basic Usage
+To use Ray orchestrator, you need to first install Ray.
+```shell
+cd examples/ray_orchestrator
+pip install -r requirements.txt
+```
+
+To run a simple `TP=2` example with a Hugging Face model:
+
+```shell
+python llm_inference_distributed_ray.py
+```
+
+This example is the same as in `/examples/llm-api`, with the only change being `orchestrator_type="ray"` on `LLM()`. Other examples can be adapted similarly by toggling this flag.
+
+
+## Features
+Currently available:
+- Generate text asynchronously (refer to [llm_inference_async_ray.py](/examples/ray_orchestrator/llm_inference_async_ray.py))
+- Multi-node inference (refer to [multi-node README](/examples/ray_orchestrator/multi_nodes/README.md))
+- Disaggregated serving (refer to [disagg README](/examples/ray_orchestrator/disaggregated/README.md))
+
+*Initial testing has been focused on LLaMA and DeepSeek variants. Please open an Issue if you encounter problems with other models so we can prioritize support.*
+
+## Roadmap
+- Performance optimization
+- Integration with RLHF frameworks, such as [Verl](https://github.com/volcengine/verl) and [NVIDIA NeMo-RL](https://github.com/NVIDIA-NeMo/RL).
+
+## Architecture
+This feature introduces new classes such as [RayExecutor](/tensorrt_llm/executor/ray_executor.py) and [RayGPUWorker](/tensorrt_llm/executor/ray_gpu_worker.py) for Ray actor lifecycle management and distributed inference. In Ray mode, collective ops run on [torch.distributed](https://docs.pytorch.org/tutorials/beginner/dist_overview.html) without MPI. We welcome contributions to improve and extend this support.
+
+![Ray orchestrator architecture](/docs/source/media/ray_orchestrator_architecture.jpg)

docs/source/index.rst

@@ -73,6 +73,7 @@ Welcome to TensorRT LLM's Documentation!
    features/speculative-decoding.md
    features/checkpoint-loading.md
    features/auto_deploy/auto-deploy.md
+   features/ray-orchestrator.md
 
 .. toctree::
    :maxdepth: 2

examples/ray_orchestrator/README.md

@@ -18,7 +18,7 @@ cd examples/ray_orchestrator
 pip install -r requirements.txt
 ```
 
-Run a simple `TP=2` example with a Hugging Face model:
+To run a simple `TP=2` example with a Hugging Face model:
 
 ```shell
 python llm_inference_distributed_ray.py
@@ -33,11 +33,11 @@ This example is the same as in `/examples/llm-api`, with the only change being `
 - Multi-node inference (refer to [multi-node README](./multi_nodes/README.md))
 - Disaggregated serving (refer to [disagg README](./disaggregated/README.md))
 
-**Initial testing has been focused on LLaMA and DeepSeek variants. Please open an Issue if you encounter problems with other models so we can prioritize support.**
+*Initial testing has been focused on LLaMA and DeepSeek variants. Please open an Issue if you encounter problems with other models so we can prioritize support.*
 
 ### Upcoming
 - Performance optimization
-- Integration with RLHF frameworks, such as [NVIDIA Nemo-RL](https://github.com/NVIDIA-NeMo/RL) and [Verl](https://github.com/volcengine/verl).
+- Integration with RLHF frameworks, such as [Verl](https://github.com/volcengine/verl) and [NVIDIA Nemo-RL](https://github.com/NVIDIA-NeMo/RL).
 
 ## Architecture
 This feature introduces new classes such as [RayExecutor](/tensorrt_llm/executor/ray_executor.py) and [RayGPUWorker](/tensorrt_llm/executor/ray_gpu_worker.py) for Ray actor lifecycle management and distributed inference. In Ray mode, collective ops run on [torch.distributed](https://docs.pytorch.org/tutorials/beginner/dist_overview.html) without MPI. We welcome contributions to improve and extend this support.