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Well-lit Path: Wide Expert Parallelism (EP/DP) with LeaderWorkerSet

Overview​

This guide demonstrates how to deploy DeepSeek-R1-0528 using vLLM's P/D disaggregation support with NIXL in a wide expert parallel pattern with LeaderWorkerSets. This guide has been validated on:

  • a 32xH200 cluster with InfiniBand networking
  • a 32xH200 cluster on GKE with RoCE networking
  • a 32xB200 cluster on GKE with RoCE networking

WARNING: We are still investigating and optimizing performance for other hardware and networking configurations

In this example, we will demonstrate a deployment of DeepSeek-R1-0528 with:

  • 1 DP=16 Prefill Worker
  • 1 DP=16 Decode Worker

Hardware Requirements​

This guide requires 32 Nvidia H200 or B200 GPUs and InfiniBand or RoCE RDMA networking. Check modelserver/base/decode.yaml and modelserver/base/prefill.yaml for detailed resource requirements.

Prerequisites​

Installation​

Use the helmfile to compose and install the stack. The Namespace in which the stack will be deployed will be derived from the ${NAMESPACE} environment variable. If you have not set this, it will default to llm-d-wide-ep in this example.

# Clone the repo and switch to the latest release tag 
tag=$(curl -s https://api.github.com/repos/llm-d/llm-d/releases/latest | jq -r '.tag_name')
git clone https://github.com/llm-d/llm-d.git && cd llm-d && git checkout "$tag"

export NAMESPACE=llm-d-wide-ep # or any other namespace
cd guides/wide-ep-lws/
kubectl create namespace ${NAMESPACE}

Deploy Model Servers​

GKE and CoreWeave are tested Kubernetes providers for this well-lit path. You can customize the manifests if you run on other Kubernetes providers.

GKE (H200)​

kubectl apply -k ./manifests/modelserver/gke -n ${NAMESPACE}

Deploy InferencePool​

Select the provider-specific Helm command using the tabs below.

GKE​

helm install deepseek-r1 \
  -n ${NAMESPACE} \
  -f inferencepool.values.yaml \
  --set "provider.name=gke" \
  --set "inferencePool.apiVersion=inference.networking.k8s.io/v1" \
  --set "inferenceExtension.monitoring.gke.enable=true" \
  oci://us-central1-docker.pkg.dev/k8s-staging-images/gateway-api-inference-extension/charts/inferencepool \
  --version v1.2.0-rc.1

Deploy Gateway and HTTPRoute​

Choose the gateway manifest that matches your environment.

GKE (Regional External)​

kubectl apply -k ./manifests/gateway/gke-l7-regional-external-managed -n ${NAMESPACE}

Gateway options​

To see what gateway options are supported refer to our gateway provider prereq doc. Gateway configurations per provider are tracked in the gateway-configurations directory.

You can also customize your gateway, for more information on how to do that see our gateway customization docs.

Tuning Selective PD​

As with PD, the wide-ep-lws guide supports selective PD. For information on this refer to this section of the PD docs.

Verifying the installation​

  • Firstly, you should be able to list all helm releases installed into your chosen namespace:
helm list -n ${NAMESPACE}
NAME            NAMESPACE       REVISION    UPDATED                                 STATUS      CHART                       APP VERSION
deepseek-r1     llm-d-wide-ep   1           2025-08-24 13:14:53.355639 -0700 PDT    deployed    inferencepool-v1.0          v0.3.0
  • Out of the box with this example you should have the following resources (if using Istio):
kubectl get all -n ${NAMESPACE}
NAME                                                         READY   STATUS    RESTARTS   AGE
pod/infra-wide-ep-inference-gateway-istio-74d5c66c86-h5mfn   1/1     Running   0          2m22s
pod/wide-ep-llm-d-decode-0                   2/2     Running   0          2m13s
pod/wide-ep-llm-d-decode-0-1                 2/2     Running   0          2m13s
pod/deepseek-r1-epp-84dd98f75b-r6lvh         1/1     Running   0          2m14s
pod/wide-ep-llm-d-prefill-0                  1/1     Running   0          2m13s
pod/wide-ep-llm-d-prefill-0-1                1/1     Running   0          2m13s


NAME                                            TYPE           CLUSTER-IP    EXTERNAL-IP   PORT(S)                        AGE
service/infra-wide-ep-inference-gateway-istio   ClusterIP      10.16.1.34    10.16.4.2     15021:30312/TCP,80:33662/TCP   2m22s
service/wide-ep-ip-1e480070                     ClusterIP      None          <none>        54321/TCP                      2d4h
service/wide-ep-llm-d-decode                    ClusterIP      None          <none>        <none>                         2m13s
service/deepseek-r1-epp                         ClusterIP      10.16.1.137   <none>        9002/TCP                       2d4h
service/wide-ep-llm-d-prefill                   ClusterIP      None          <none>        <none>                         2m13s

NAME                                                    READY   UP-TO-DATE   AVAILABLE   AGE
deployment.apps/infra-wide-ep-inference-gateway-istio   1/1     1            1           2m22s
deployment.apps/deepseek-r1-epp       1/1     1            1           2m14s

NAME                                                               DESIRED   CURRENT   READY   AGE
replicaset.apps/infra-wide-ep-inference-gateway-istio-74d5c66c86   1         1         1       2m22s
replicaset.apps/deepseek-r1-epp-55bb9857cf       1         1         1       2m14s

NAME                                                      READY   AGE
statefulset.apps/wide-ep-llm-d-decode     1/1     2m13s
statefulset.apps/wide-ep-llm-d-decode-0   1/1     2m13s
statefulset.apps/wide-ep-llm-d-prefill    1/1     2m13s
statefulset.apps/wide-ep-llm-d-prefill-1  1/1     2m13s

NOTE: This assumes no other guide deployments in your given ${NAMESPACE} and you have not changed the default release names via the ${RELEASE_NAME} environment variable.

Using the stack​

For instructions on getting started making inference requests see our docs

NOTE: This example particularly benefits from utilizing stern as described in the getting-started-inferencing docs, because while we only have 3 inferencing pods, it has 16 vllm servers or ranks.

NOTE: Compared to the other examples, this one takes anywhere between 7-10 minutes for the vllm API servers to startup so this might take longer before you can interact with this example.

Cleanup​

To remove the deployment:

# From examples/wide-ep-lws
helm uninstall deepseek-r1 -n ${NAMESPACE}
kubectl delete -k ./manifests/modelserver/<gke|coreweave> -n ${NAMESPACE}
kubectl delete -k ./manifests/gateway/<gke-l7-regional-external-managed|istio|kgateway|kgateway-openshift> -n ${NAMESPACE}

Customization​

For information on customizing a guide and tips to build your own, see our docs

Documentation Version

This documentation corresponds to llm-d v0.4.0, the latest public release. For the most current development changes, see this file on main.

📝 To suggest changes or report issues, please create an issue.

Source: guides/wide-ep-lws/README.md