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Welcome to NVIDIA Dynamo
========================

NVIDIA Dynamo is a high-throughput low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.

Dive in: Examples
-----------------------

.. grid:: 1 2 2 2
    :gutter: 3
    :margin: 0
    :padding: 3 4 0 0

    .. grid-item-card:: :doc:`Hello World </examples/hello_world>`
        :link: /examples/hello_world
        :link-type: doc

        Demonstrates the basic concepts of Dynamo by creating a simple multi-service pipeline.

    .. grid-item-card:: :doc:`LLM Deployment </examples/llm_deployment>`
        :link: /examples/llm_deployment
        :link-type: doc

        Presents examples and reference implementations for deploying Large Language Models (LLMs) in various configurations.

    .. grid-item-card:: :doc:`Multinode </examples/multinode>`
        :link: /examples/multinode
        :link-type: doc

        Demonstrates deployment for disaggregated serving on 3 nodes using `nvidia/Llama-3.1-405B-Instruct-FP8`.

    .. grid-item-card:: :doc:`TensorRT-LLM </examples/trtllm>`
        :link: /examples/trtllm
        :link-type: doc

        Presents TensorRT-LLM examples and reference implementations for deploying Large Language Models (LLMs) in various configurations.

Overview
--------

The NVIDIA Dynamo Platform is a high-performance, low-latency inference platform
designed to serve all AI models—across any framework, architecture, or deployment scale.
Dynamo is inference engine agnostic, supporting TRT-LLM, vLLM, SGLang, and others, and captures
LLM-specific capabilities such as:

* **Disaggregated prefill & decode inference** - Maximizes GPU throughput and facilitates trade off between throughput and latency.
* **Dynamic GPU scheduling** - Optimizes performance based on fluctuating demand.
* **LLM-aware request routing** - Eliminates unnecessary KV cache re-computation.
* **Accelerated data transfer** - Reduces inference response time using NIXL.
* **KV cache offloading** - Leverages several memory hierarchies for higher system throughput.

Built in Rust for performance and in Python for extensibility, Dynamo is fully open-source
and driven by a transparent, OSS (Open Source Software) first development approach.

.. toctree::
   :hidden:

   Welcome to Dynamo <self>
   Support Matrix <support_matrix.md>
   Getting Started <get_started.md>

.. toctree::
   :hidden:
   :caption: Architecture & Features

   High Level Architecture <architecture/architecture.md>
   Distributed Runtime <architecture/distributed_runtime.md>
   Disaggregated Serving <architecture/disagg_serving.md>
   KV Block Manager <architecture/kvbm_intro.rst>
   KV Cache Routing <architecture/kv_cache_routing.md>
   Planner <guides/planner.md>

.. toctree::
   :hidden:
   :caption: Dynamo Command Line Interface

   CLI Overview <guides/cli_overview.md>
   Running Dynamo (dynamo run) <guides/dynamo_run.md>
   Serving Inference Graphs (dynamo serve) <guides/dynamo_serve.md>
   Building Dynamo (dynamo build) <guides/dynamo_build.md>
   Deploying Inference Graphs (dynamo deploy) <guides/dynamo_deploy/README.md>

.. toctree::
   :hidden:
   :caption: Usage Guides

   Writing Python Workers in Dynamo <guides/backend.md>
   Disaggregation and Performance Tuning <guides/disagg_perf_tuning.md>
   KV Cache Router Performance Tuning <guides/kv_router_perf_tuning.md>
   Planner Benchmark Example <guides/planner_benchmark/benchmark_planner.md>

.. toctree::
   :hidden:
   :caption: Deployment Guides

   Dynamo Cloud Kubernetes Platform <guides/dynamo_deploy/dynamo_cloud.md>
   Deploying Dynamo Inference Graphs to Kubernetes using the Dynamo Cloud Platform <guides/dynamo_deploy/operator_deployment.md>
   Manual Helm Deployment <guides/dynamo_deploy/manual_helm_deployment.md>
   Minikube Setup Guide <guides/dynamo_deploy/minikube.md>

.. toctree::
   :hidden:
   :caption: API

   SDK Reference <API/sdk.md>
   Python API <API/python_bindings.md>

.. toctree::
   :hidden:
   :caption: Examples

   Hello World Example <examples/hello_world.md>
   LLM Deployment Examples <examples/llm_deployment.md>
   Multinode Examples <examples/multinode.md>
   LLM Deployment Examples using TensorRT-LLM <examples/trtllm.md>