Fix all broken links caught by lychee

README.md

@@ -183,7 +183,7 @@ Run the backend/worker like this:
 python -m dynamo.sglang.worker --help
 ```
 
-You can pass any sglang flags directly to this worker, see https://docs.sglang.ai/backend/server_arguments.html . See there to use multiple GPUs.
+You can pass any sglang flags directly to this worker, see https://docs.sglang.ai/advanced_features/server_arguments.html . See there to use multiple GPUs.
 
 ## TensorRT-LLM
 

benchmarks/llm/README.md

@@ -40,7 +40,7 @@ This guide provides detailed steps on benchmarking Large Language Models (LLMs)
  3. Start NATS and ETCD
 
     ```bash
-    docker compose -f deploy/metrics/docker-compose.yml up -d
+    docker compose -f deploy/docker-compose.yml up -d
     ```
 
 > [!NOTE]
@@ -312,27 +312,14 @@ With the Pareto Frontiers of the baseline and the disaggregated results plotted
 greatest increase in throughput (along the y-axis) between the baseline and the disaggregated result Pareto Frontier,
 over different latencies (along the x-axis).
 
-For example, at 45 tokens/s/user, the increase in tokens/s/gpu is `145 - 80 = 65`, from the orange baseline to the
-blue disaggregated line, so the improvement is around 1.44x speed up:
-![Example Pareto Plot](./example_plots/single_node_pareto_plot.png)
-Note: The above example was collected over a single benchmarking run, the actual number may vary between runs, configurations and hardware.
-
 ## Supporting Additional Models
 
 The instructions above can be used for nearly any model desired.
 More complex setup instructions might be required for certain models.
 The above instruction regarding ETCD, NATS, nginx, dynamo-serve, and GenAI-Perf still apply and can be reused.
 The specifics of deploying with different hardware, in a unique environment, or using another model framework can be adapted using the links below.
 
-Regardless of the deployment mechanism, the GenAI-Perf tool will report the same metrics and measurements so long as an accessible endpoint is available for it to interact with. Use the provided [perf.sh](../../../benchmarks/llm/perf.sh) script to automate the measurement of model throughput and latency against multiple request concurrences.
-
-### Deployment Examples
-
-- [Dynamo Multinode Deployments](../../../docs/examples/multinode.md)
-- [Dynamo TensorRT LLM Deployments](../../../docs/examples/trtllm.md)
-    - [Aggregated Deployment of Very Large Models](../../../docs/examples/multinode.md#aggregated-deployment)
-- [Dynamo vLLM Deployments](../../../docs/examples/llm_deployment.md)
-
+Regardless of the deployment mechanism, the GenAI-Perf tool will report the same metrics and measurements so long as an accessible endpoint is available for it to interact with. Use the provided [perf.sh](perf.sh) script to automate the measurement of model throughput and latency against multiple request concurrences.
 
 ## Monitor Benchmark Startup Status
 
@@ -388,7 +375,7 @@ when the script is invoked, it will:
 ## Metrics and Visualization
 
 For instructions on how to acquire per worker metrics and visualize them using Grafana,
-please see the provided [Visualization with Prometheus and Grafana](../../../deploy/metrics/README.md).
+please see the provided [Visualization with Prometheus and Grafana](../../docs/guides/metrics.md).
 
 ## Troubleshooting
 

components/README.md

@@ -77,4 +77,4 @@ To get started with Dynamo components:
 4. **Run deployment scripts** from the engine's launch directory
 5. **Monitor performance** using the metrics component
 
-For detailed instructions, see the README files in each component directory and the main [Dynamo documentation](../../docs/).
+For detailed instructions, see the README files in each component directory and the main [Dynamo documentation](../docs/).

components/backends/sglang/README.md

@@ -52,7 +52,7 @@ git checkout $(git describe --tags $(git rev-list --tags --max-count=1))
 
 ## Quick Start
 
-Below we provide a guide that lets you run all of our the common deployment patterns on a single node. See our different [architectures](../llm/README.md#deployment-architectures) for a high level overview of each pattern and the architecture diagram for each.
+Below we provide a guide that lets you run all of our the common deployment patterns on a single node.
 
 ### Start NATS and ETCD in the background
 

components/backends/sglang/deploy/README.md

@@ -74,7 +74,7 @@ extraPodSpec:
 
 Before using these templates, ensure you have:
 
-1. **Dynamo Cloud Platform installed** - See [Installing Dynamo Cloud](../../docs/guides/dynamo_deploy/dynamo_cloud.md)
+1. **Dynamo Cloud Platform installed** - See [Installing Dynamo Cloud](../../../../docs/guides/dynamo_deploy/dynamo_cloud.md)
 2. **Kubernetes cluster with GPU support**
 3. **Container registry access** for SGLang runtime images
 4. **HuggingFace token secret** (referenced as `envFromSecret: hf-token-secret`)
@@ -159,4 +159,4 @@ Common issues and solutions:
 3. **Health check failures**: Review model loading logs and increase `initialDelaySeconds`
 4. **Out of memory**: Increase memory limits or reduce model batch size
 
-For additional support, refer to the [deployment troubleshooting guide](../../docs/guides/dynamo_deploy/quickstart.md#troubleshooting).
+For additional support, refer to the [deployment troubleshooting guide](../../../../docs/guides/dynamo_deploy/quickstart.md#troubleshooting).

components/backends/sglang/docs/dsr1-wideep-h100.md

@@ -5,7 +5,7 @@ SPDX-License-Identifier: Apache-2.0
 
 # Running DeepSeek-R1 Disaggregated with WideEP on H100s
 
-Dynamo supports SGLang's implementation of wide expert parallelism and large scale P/D for DeepSeek-R1! You can read their blog post [here](https://www.nvidia.com/en-us/technologies/ai/deepseek-r1-large-scale-p-d-with-wide-expert-parallelism/) for more details. We provide a Dockerfile for this in `container/Dockerfile.sglang-deepep` and configurations to deploy this at scale. In this example, we will run 1 prefill worker on 4 H100 nodes and 1 decode worker on 9 H100 nodes (104 total GPUs).
+Dynamo supports SGLang's implementation of wide expert parallelism and large scale P/D for DeepSeek-R1! You can read their blog post [here](https://lmsys.org/blog/2025-05-05-large-scale-ep/) for more details. We provide a Dockerfile for this in `container/Dockerfile.sglang-deepep` and configurations to deploy this at scale. In this example, we will run 1 prefill worker on 4 H100 nodes and 1 decode worker on 9 H100 nodes (104 total GPUs).
 
 ## Instructions
 

components/backends/sglang/slurm_jobs/README.md

@@ -1,10 +1,10 @@
 # Example: Deploy Multi-node SGLang with Dynamo on SLURM
 
-This folder implements the example of [SGLang DeepSeek-R1 Disaggregated with WideEP](../dsr1-wideep.md) on a SLURM cluster.
+This folder implements the example of [SGLang DeepSeek-R1 Disaggregated with WideEP](../docs/dsr1-wideep-h100.md) on a SLURM cluster.
 
 ## Overview
 
-The scripts in this folder set up multiple cluster nodes to run the [SGLang DeepSeek-R1 Disaggregated with WideEP](../dsr1-wideep.md) example, with separate nodes handling prefill and decode.
+The scripts in this folder set up multiple cluster nodes to run the [SGLang DeepSeek-R1 Disaggregated with WideEP](../docs/dsr1-wideep-h100.md) example, with separate nodes handling prefill and decode.
 The node setup is done using Python job submission scripts with Jinja2 templates for flexible configuration. The setup also includes GPU utilization monitoring capabilities to track performance during benchmarks.
 
 ## Scripts
@@ -57,7 +57,7 @@ For simplicity of the example, we will make some assumptions about your SLURM cl
    If your cluster supports similar container based plugins, you may be able to
    modify the template to use that instead.
 3. We assume you have already built a recent Dynamo+SGLang container image as
-   described [here](../dsr1-wideep.md#instructions).
+   described [here](../docs/dsr1-wideep-h100.md#instructions).
    This is the image that can be passed to the `--container-image` argument in later steps.
 
 ## Usage

components/backends/trtllm/README.md

@@ -193,7 +193,7 @@ For complete Kubernetes deployment instructions, configurations, and troubleshoo
 
 ### Client
 
-See [client](../llm/README.md#client) section to learn how to send request to the deployment.
+See [client](../vllm/README.md#client) section to learn how to send request to the deployment.
 
 NOTE: To send a request to a multi-node deployment, target the node which is running `python3 -m dynamo.frontend <args>`.
 
@@ -233,7 +233,7 @@ This allows a request to be migrated up to 3 times before failing. See the [Requ
 
 ## Client
 
-See [client](../llm/README.md#client) section to learn how to send request to the deployment.
+See [client](../vllm/README.md#client) section to learn how to send request to the deployment.
 
 NOTE: To send a request to a multi-node deployment, target the node which is running `python3 -m dynamo.frontend <args>`.
 

components/backends/trtllm/deploy/README.md

@@ -211,7 +211,7 @@ envs:
 
 ## Testing the Deployment
 
-Send a test request to verify your deployment. See the [client section](../../../../components/backends/llm/README.md#client) for detailed instructions.
+Send a test request to verify your deployment. See the [client section](../../../../components/backends/vllm/README.md#client) for detailed instructions.
 
 **Note:** For multi-node deployments, target the node running `python3 -m dynamo.frontend <args>`.
 

components/backends/trtllm/gpt-oss.md

@@ -345,7 +345,7 @@ flowchart TD
 
 ## Next Steps
 
-- **Production Deployment**: For multi-node deployments, see the [Multi-node Guide](../../examples/basics/multinode/README.md)
+- **Production Deployment**: For multi-node deployments, see the [Multi-node Guide](../../../examples/basics/multinode/README.md)
 - **Advanced Configuration**: Explore TensorRT-LLM engine building options for further optimization
 - **Monitoring**: Set up Prometheus and Grafana for production monitoring
 - **Performance Benchmarking**: Use GenAI-Perf to measure and optimize your deployment performance

components/backends/vllm/LMCache_Integration.md

@@ -166,5 +166,5 @@ lmcache_config = {
 ## References and Additional Resources
 
 - [LMCache Documentation](https://docs.lmcache.ai/index.html) - Comprehensive guide and API reference
-- [Configuration Reference](https://docs.lmcache.ai/api_reference/config.html) - Detailed configuration options
+- [Configuration Reference](https://docs.lmcache.ai/api_reference/configurations.html) - Detailed configuration options
 

components/metrics/README.md

@@ -18,7 +18,6 @@ The deprecated `metrics` component is a utility for collecting, aggregating, and
 
 **Note**: This is a demo implementation. The deprecated `metrics` component is no longer under active development.
 - In this demo the metrics names use the prefix "llm", but in production they will be prefixed with "dynamo" (e.g., the HTTP `/metrics` endpoint will serve metrics with "dynamo" prefixes)
-- This demo will only work when using examples/llm/configs/agg.yml-- other configurations will not work
 
 <div align="center">
   <img src="images/dynamo_metrics_grafana.png" alt="Dynamo Metrics Dashboard"/>
@@ -81,8 +80,7 @@ metrics --component MyComponent --endpoint my_endpoint
 
 ### Real Worker
 
-To run a more realistic deployment to gathering metrics from,
-see the examples in [examples/llm](../../examples/llm).
+To run a more realistic deployment to gather metrics:
 
 ```bash
 python -m dynamo.frontend &

deploy/metrics/README.md

@@ -68,7 +68,7 @@ When using Dynamo HTTP Frontend (`--framework VLLM` or `--framework TRTLLM`), th
 ### Required Files
 
 The following configuration files should be present in this directory:
-- [docker-compose.yml](./docker-compose.yml): Defines the Prometheus and Grafana services
+- [docker-compose.yml](../docker-compose.yml): Defines the Prometheus and Grafana services
 - [prometheus.yml](./prometheus.yml): Contains Prometheus scraping configuration
 - [grafana-datasources.yml](./grafana-datasources.yml): Contains Grafana datasource configuration
 - [grafana_dashboards/grafana-dashboard-providers.yml](./grafana_dashboards/grafana-dashboard-providers.yml): Contains Grafana dashboard provider configuration

docs/API/nixl_connect/README.md

@@ -103,7 +103,7 @@ flowchart LR
 
 ### Multimodal Example
 
-In the case of the [Dynamo Multimodal Disaggregated Example](../../examples/multimodal/README.md):
+In the case of the [Dynamo Multimodal Disaggregated Example](../../../examples/multimodal/README.md):
 
  1. The HTTP frontend accepts a text prompt and a URL to an image.
 
@@ -153,11 +153,11 @@ flowchart LR
 
 #### Code Examples
 
-See [prefill_worker](https://github.com/ai-dynamo/dynamo/tree/main/examples/multimodal/components/prefill_worker.py#L199) or [decode_worker](https://github.com/ai-dynamo/dynamo/tree/main/examples/multimodal/components/decode_worker.py#L239) from our Multimodal example,
+See [prefill_worker](../../../examples/multimodal/components/worker.py) or [decode_worker](../../../examples/multimodal/components/worker.py) from our Multimodal example,
 for how they coordinate directly with the Encode Worker by creating a [`WritableOperation`](writable_operation.md),
 sending the operation's metadata via Dynamo's round-robin dispatcher, and awaiting the operation for completion before making use of the transferred data.
 
-See [encode_worker](https://github.com/ai-dynamo/dynamo/tree/main/examples/multimodal/components/encode_worker.py#L190) from our Multimodal example,
+See [encode_worker](../../..//examples/multimodal/components/encode_worker.py#L190) from our Multimodal example,
 for how the resulting embeddings are registered with the NIXL subsystem by creating a [`Descriptor`](descriptor.md),
 a [`WriteOperation`](write_operation.md) is created using the metadata provided by the requesting worker,
 and the worker awaits for the data transfer to complete for yielding a response.
@@ -170,15 +170,13 @@ and the worker awaits for the data transfer to complete for yielding a response.
   - [Device](device.md)
   - [ReadOperation](read_operation.md)
   - [ReadableOperation](readable_operation.md)
-  - [SerializedRequest](serialized_request.md)
   - [WritableOperation](writable_operation.md)
   - [WriteOperation](write_operation.md)
 
 
 ## References
 
   - [NVIDIA Dynamo](https://developer.nvidia.com/dynamo) @ [GitHub](https://github.com/ai-dynamo/dynamo)
-    - [NVIDIA Dynamo NIXL Connect](https://github.com/ai-dynamo/dynamo/tree/main/docs/runtime/nixl_connect)
   - [NVIDIA Inference Transfer Library (NIXL)](https://developer.nvidia.com/blog/introducing-nvidia-dynamo-a-low-latency-distributed-inference-framework-for-scaling-reasoning-ai-models/#nvidia_inference_transfer_library_nixl_low-latency_hardware-agnostic_communication%C2%A0) @ [GitHub](https://github.com/ai-dynamo/nixl)
-  - [Dynamo Multimodal Example](https://github.com/ai-dynamo/dynamo/tree/main/examples/multimodal)
+  - [Dynamo Multimodal Example](../../..//examples/multimodal)
   - [NVIDIA GPU Direct](https://developer.nvidia.com/gpudirect)

docs/architecture/dynamo_flow.md

@@ -17,7 +17,7 @@ limitations under the License.
 
 # Dynamo Architecture Flow
 
-This diagram shows the NVIDIA Dynamo disaggregated inference system as implemented in [examples/llm](https://github.com/ai-dynamo/dynamo/tree/main/examples/llm). Color-coded flows indicate different types of operations:
+This diagram shows the NVIDIA Dynamo disaggregated inference system as implemented in [components/backends/vllm](../../components/backends/vllm). Color-coded flows indicate different types of operations:
 
 ## 🔵 Main Request Flow (Blue)
 The primary user journey through the system:

docs/guides/deploy/k8s_metrics.md

@@ -39,7 +39,7 @@ This will create two components:
 - A Worker component exposing metrics on its system port
 
 Both components expose a `/metrics` endpoint following the OpenMetrics format, but with different metrics appropriate to their roles. For details about:
-- Deployment configuration: See the [vLLM README](../../../../components/backends/vllm/README.md)
+- Deployment configuration: See the [vLLM README](../../components/backends/vllm/README.md)
 - Available metrics: See the [metrics guide](../metrics.md)
 
 ### Validate the Deployment
@@ -62,7 +62,7 @@ curl localhost:8080/v1/chat/completions \
   }'
 ```
 
-For more information about validating the deployment, see the [vLLM README](../../../../components/backends/vllm/README.md).
+For more information about validating the deployment, see the [vLLM README](../../components/backends/vllm/README.md).
 
 ## Set Up Metrics Collection
 

docs/guides/dynamo_deploy/README.md

@@ -54,7 +54,7 @@ You can use `kubectl get dynamoGraphDeployment -n ${NAMESPACE}` to view your dep
 You can use `kubectl delete dynamoGraphDeployment <your-dep-name> -n ${NAMESPACE}` to delete the deployment.
 
 We provide a Custom Resource YAML file for many examples under the `deploy/` folder.
-Use [VLLM YAML](../../components/backends/vllm/deploy/agg.yaml) for an example.
+Use [VLLM YAML](../../../components/backends/vllm/deploy/agg.yaml) for an example.
 
 **Note 1** Example Image
 

docs/guides/dynamo_deploy/model_caching_with_fluid.md

@@ -318,7 +318,7 @@ spec:
 
 - [Fluid Documentation](https://fluid-cloudnative.github.io/)
 - [Alluxio Documentation](https://docs.alluxio.io/)
-- [MinIO Documentation](https://min.io/docs/)
+- [MinIO Documentation](https://docs.min.io/)
 - [Hugging Face Hub](https://huggingface.co/docs/hub/index)
 - [Dynamo README](https://github.com/ai-dynamo/dynamo/blob/main/.devcontainer/README.md)
 - [Dynamo Documentation](https://docs.nvidia.com/dynamo/latest/index.html)

docs/guides/dynamo_deploy/multinode-deployment.md

@@ -50,8 +50,8 @@ These systems provide enhanced scheduling capabilities including topology-aware
 
 LWS is a simple multinode deployment mechanism that allows you to deploy a workload across multiple nodes.
 
-- **LWS**: [LWS Installation](https://github.com/NVIDIA/LWS#installation)
-- **Volcano**: [Volcano Installation](https://volcano.sh/docs/installation/install-volcano/)
+- **LWS**: [LWS Installation](https://github.com/kubernetes-sigs/lws#installation)
+- **Volcano**: [Volcano Installation](https://volcano.sh/en/docs/installation/)
 
 Volcano is a Kubernetes native scheduler optimized for AI workloads at scale. It is used in conjunction with LWS to provide gang scheduling support.
 
@@ -110,8 +110,8 @@ args:
 
 For additional support and examples, see the working multinode configurations in:
 
-- **SGLang**: [components/backends/sglang/deploy/](../../components/backends/sglang/deploy/)
-- **TensorRT-LLM**: [components/backends/trtllm/deploy/](../../components/backends/trtllm/deploy/)
-- **vLLM**: [components/backends/vllm/deploy/](../../components/backends/vllm/deploy/)
+- **SGLang**: [components/backends/sglang/deploy/](../../../components/backends/sglang/deploy/)
+- **TensorRT-LLM**: [components/backends/trtllm/deploy/](../../../components/backends/trtllm/deploy/)
+- **vLLM**: [components/backends/vllm/deploy/](../../../components/backends/vllm/deploy/)
 
-These examples demonstrate proper usage of the `multinode` section with corresponding `gpu` limits and correct `tp-size` configuration.
+These examples demonstrate proper usage of the `multinode` section with corresponding `gpu` limits and correct `tp-size` configuration.

docs/guides/dynamo_deploy/quickstart.md

@@ -67,7 +67,7 @@ Ensure you have the source code checked out and are in the `dynamo` directory:
 
 ### Set Environment Variables
 
-Our examples use the [`nvcr.io`](https://nvcr.io/nvidia/ai-dynamo/) but you can setup your own values if you use another docker registry.
+Our examples use the [`nvcr.io`](https://catalog.ngc.nvidia.com) but you can setup your own values if you use another docker registry.
 
 ```bash
 export NAMESPACE=dynamo-cloud # or whatever you prefer.

examples/basics/disaggregated_serving/README.md

@@ -36,9 +36,9 @@ docker compose -f deploy/metrics/docker-compose.yml up -d
 
 ## Components
 
-- [Frontend](../../../components/frontend/README) - HTTP API endpoint that receives requests and forwards them to the decode worker
-- [vLLM Prefill Worker](../../../components/backends/vllm/README) - Specialized worker for prefill phase execution
-- [vLLM Decode Worker](../../../components/backends/vllm/README) - Specialized worker that handles requests and decides between local/remote prefill
+- [Frontend](../../../components/frontend/README.md) - HTTP API endpoint that receives requests and forwards them to the decode worker
+- [vLLM Prefill Worker](../../../components/backends/vllm/README.md) - Specialized worker for prefill phase execution
+- [vLLM Decode Worker](../../../components/backends/vllm/README.md) - Specialized worker that handles requests and decides between local/remote prefill
 
 ```mermaid
 ---