fix: Address QA issues + product feedback

README.md

@@ -21,12 +21,30 @@ limitations under the License.
 [![Discord](https://dcbadge.limes.pink/api/server/D92uqZRjCZ?style=flat)](https://discord.gg/D92uqZRjCZ)
 [![Ask DeepWiki](https://deepwiki.com/badge.svg)](https://deepwiki.com/ai-dynamo/dynamo)
 
-| **[Roadmap](https://github.com/ai-dynamo/dynamo/issues/762)** | **[Documentation](https://docs.nvidia.com/dynamo/latest/index.html)** | **[Examples](https://github.com/ai-dynamo/dynamo/tree/main/examples)** | **[Design Proposals](https://github.com/ai-dynamo/enhancements)** |
+| **[Roadmap](https://github.com/ai-dynamo/dynamo/issues/762)** | **[Documentation](https://docs.nvidia.com/dynamo/latest/index.html)** | **[Support Matrix](docs/support_matrix.md)** | **[Examples](https://github.com/ai-dynamo/dynamo/tree/main/examples)** | **[Design Proposals](https://github.com/ai-dynamo/enhancements)** |
 
 # NVIDIA Dynamo
 
 High-throughput, low-latency inference framework designed for serving generative AI and reasoning models in multi-node distributed environments.
 
+## Framework Support Matrix
+
+| Feature | vLLM | SGLang | TensorRT-LLM |
+|---------|----------------------|----------------------------|----------------------------------------|
+| [**Disaggregated Serving**](/docs/architecture/disagg_serving.md) | ✅ | ✅ | ✅ |
+| [**Conditional Disaggregation**](/docs/architecture/disagg_serving.md#conditional-disaggregation) | 🚧 | 🚧 | 🚧 |
+| [**KV-Aware Routing**](/docs/architecture/kv_cache_routing.md) | ✅ | ✅ | ✅ |
+| [**SLA-Based Planner**](/docs/architecture/sla_planner.md) | ✅ | 🚧 | 🚧 |
+| [**Load Based Planner**](/docs/architecture/load_planner.md) | ✅ | 🚧 | 🚧 |
+| [**KVBM**](/docs/architecture/kvbm_architecture.md) | 🚧 | 🚧 | 🚧 |
+
+To learn more about each framework and their capabilities, check out each framework's README and deploy them with Dynamo!
+- **[vLLM](components/backends/vllm/README.md)**
+- **[SGLang](components/backends/sglang/README.md)**
+- **[TensorRT-LLM](components/backends/trtllm/README.md)**
+
+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.
+
 ## The Era of Multi-GPU, Multi-Node
 
 <p align="center">
@@ -47,24 +65,6 @@ Dynamo is designed to be inference engine agnostic (supports TRT-LLM, vLLM, SGLa
   <img src="./docs/images/frontpage-architecture.png" alt="Dynamo architecture" width="600" />
 </p>
 
-## Framework Support Matrix
-
-| Feature | vLLM | SGLang | TensorRT-LLM |
-|---------|----------------------|----------------------------|----------------------------------------|
-| [**Disaggregated Serving**](/docs/architecture/disagg_serving.md) | ✅ | ✅ | ✅ |
-| [**Conditional Disaggregation**](/docs/architecture/disagg_serving.md#conditional-disaggregation) | 🚧 | 🚧 | 🚧 |
-| [**KV-Aware Routing**](/docs/architecture/kv_cache_routing.md) | ✅ | ✅ | ✅ |
-| [**SLA-Based Planner**](/docs/architecture/sla_planner.md) | ✅ | 🚧 | 🚧 |
-| [**Load Based Planner**](/docs/architecture/load_planner.md) | ✅ | 🚧 | 🚧 |
-| [**KVBM**](/docs/architecture/kvbm_architecture.md) | 🚧 | 🚧 | 🚧 |
-
-To learn more about each framework and their capabilities, check out each framework's README!
-- **[vLLM](components/backends/vllm/README.md)**
-- **[SGLang](components/backends/sglang/README.md)**
-- **[TensorRT-LLM](components/backends/trtllm/README.md)**
-
-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.
-
 # Installation
 
 The following examples require a few system level packages.
@@ -167,10 +167,15 @@ To specify which GPUs to use set environment variable `CUDA_VISIBLE_DEVICES`.
 
 ## SGLang
 
+
 ```
-# Install libnuma
+# Install libnuma-dev
 apt install -y libnuma-dev
 
+# Install flashinfer-python pre-release (required by sglang for optimized inference)
+uv pip install "flashinfer-python==0.2.9rc2" --prerelease=allow
+
+# Install ai-dynamo with sglang support
 uv pip install ai-dynamo[sglang]
 ```
 

container/Dockerfile.sglang

@@ -469,4 +469,4 @@ COPY ATTRIBUTION* LICENSE /workspace/
 ENV PYTHONPATH=/workspace/examples/sglang/utils:$PYTHONPATH
 
 ENTRYPOINT ["/opt/nvidia/nvidia_entrypoint.sh"]
-CMD []
+CMD []

examples/README.md

@@ -22,6 +22,15 @@ This directory contains practical examples demonstrating how to deploy and use D
 > **Want to see a specific example?**
 > Open a [GitHub issue](https://github.com/ai-dynamo/dynamo/issues) to request an example you'd like to see, or [open a pull request](https://github.com/ai-dynamo/dynamo/pulls) if you'd like to contribute your own!
 
+## Framework Support
+
+The /examples directory shows how Dynamo broadly works using major inference engines.
+
+If you want to see advanced, framework-specific deployment patterns and best practices, check out the [Components Workflows](../components/backends/) directory:
+- **[vLLM](../components/backends/vllm/)** – vLLM-specific deployment and configuration
+- **[SGLang](../components/backends/sglang/)** – SGLang integration examples and workflows
+- **[TensorRT-LLM](../components/backends/trtllm/)** – TensorRT-LLM workflows and optimizations
+
 ## Basics & Tutorials
 
 Learn fundamental Dynamo concepts through these introductory examples:
@@ -67,13 +76,4 @@ Before running any examples, ensure you have:
 - **Docker & Docker Compose** - For containerized services
 - **CUDA-compatible GPU** - For LLM inference (except hello_world, which is non-GPU aware)
 - **Python 3.9++** - For client scripts and utilities
-- **Kubernetes cluster** - For any cloud deployment/K8s examples
-
-## Framework Support
-
-These examples show how Dynamo broadly works using major inference engines.
-
-If you want to see advanced, framework-specific deployment patterns and best practices, check out the [Components Workflows](../components/backends/) directory:
-- **[vLLM](../components/backends/vllm/)** – vLLM-specific deployment and configuration
-- **[SGLang](../components/backends/sglang/)** – SGLang integration examples and workflows
-- **[TensorRT-LLM](../components/backends/trtllm/)** – TensorRT-LLM workflows and optimizations
+- **Kubernetes cluster** - For any cloud deployment/K8s examples