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nv-anants merged 1 commit into from
Jul 3, 2025
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docs: merge changes from 0.3.1 release (#1543) #1759

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16 changes: 2 additions & 14 deletions docs/index.rst
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Expand Up @@ -62,19 +62,6 @@ Dive in: Examples

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

Overview
--------

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 is driven by a transparent development approach. Check out our repo at https://github.com/ai-dynamo/.

.. toctree::
:hidden:
Expand Down Expand Up @@ -120,14 +107,15 @@ and is driven by a transparent development approach. Check out our repo at https
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>
GKE Setup Guide <guides/dynamo_deploy/gke_setup.md>
Minikube Setup Guide <guides/dynamo_deploy/minikube.md>
Model Caching with Fluid <guides/dynamo_deploy/model_caching_with_fluid.md>

.. toctree::
:hidden:
:caption: Benchmarking

Planner Benchmark Example <guides/planner_benchmark/benchmark_planner.md>
Planner Benchmark Example <guides/planner_benchmark/README.md>


.. toctree::
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