[Doc] Documentation for distributed inference

docs/source/index.rst

@@ -29,7 +29,7 @@ vLLM is fast with:
 
 * State-of-the-art serving throughput
 * Efficient management of attention key and value memory with **PagedAttention**
-* Dynamic batching of incoming requests
+* Continuous batching of incoming requests
 * Optimized CUDA kernels
 
 vLLM is flexible and easy to use with:
@@ -40,7 +40,11 @@ vLLM is flexible and easy to use with:
 * Streaming outputs
 * OpenAI-compatible API server
 
-For more information, please refer to our `blog post <https://vllm.ai>`_.
+For more information:
+
+* `vLLM announcing blog post <https://vllm.ai>`_ (PagedAttention introduction)
+* `Continuous batching introduction <https://www.anyscale.com/blog/continuous-batching-llm-inference>`_ by Cade Daniel et al.
+
 
 
 Documentation
@@ -53,6 +57,12 @@ Documentation
    getting_started/installation
    getting_started/quickstart
 
+.. toctree::
+   :maxdepth: 1
+   :caption: Serving
+
+   serving/distributed_serving
+
 .. toctree::
    :maxdepth: 1
    :caption: Models

docs/source/serving/distributed_serving.rst

@@ -0,0 +1,39 @@
+.. _distributed_serving:
+
+Distributed Inference and Serving
+=================================
+
+vLLM supports distributed tensor-parallel inference and serving. Currently, we support `Megatron-LM's tensor parallel algorithm <https://arxiv.org/pdf/1909.08053.pdf>`_. We manage the distributed runtime with `Ray <https://github.com/ray-project/ray>`_. To run distributed inference, install Ray with:
+
+.. code-block:: bash
+
+    pip install ray
+
+To run multi-GPU inference with the :code:`LLM` class, set the :code:`tensor_parallel_size` argument to the number of GPUs you want to use. For example, to run inference on 4 GPUs:
+
+.. code-block:: python
+
+    from vllm import LLM
+    llm = LLM("facebook/opt-13b",
+              tensor_parallel_size=4)
+    output = llm.generate("San Franciso is a")
+
+To run multi-GPU serving, pass in the :code:`--tensor-parallel-size` argument when starting the server. For example, to run API server on 4 GPUs:
+
+.. code-block:: console
+
+    $ python -m vllm.entrypoints.api_server \
+    $     --model facebook/opt-13b \
+    $     --tensor-parallel-size 4
+
+To scale vLLM beyond a single machine, start a `Ray runtime <https://docs.ray.io/en/latest/ray-core/starting-ray.html>`_ via CLI before running vLLM:
+
+.. code-block:: console
+
+    $ # On head node
+    $ ray start --head
+
+    $ # On worker nodes
+    $ ray start --address=<ray-head-address>
+
+After that, you can run inference and serving on multiple machines by launching the vLLM process on the head node by setting :code:`tensor_parallel_size` to the number of GPUs to be the total number of GPUs across all machines.