Cosmos3 Super Reasoner

Deploy NVIDIA Cosmos3 Super Reasoner as eight FP8 aggregated workers. Two matched targets isolate routing behavior: pure round robin and round robin with a 300-second per-video session-affinity TTL.

Each worker starts through the Cosmos3 NIM entrypoint. NIM profile selection reads NIM_MODEL_TYPE=reasoner, NIM_MODEL_VARIANT=super, and NIM_PRECISION=fp8, picks the matching model profile for the detected GPU, and then launches dynamo.vllm because NIM_DYNAMO_WORKER=true. The FP8 Super model fits on one H200, so every GPU runs an independent replica. Each replica keeps its own multimodal processor cache, which makes routing the only difference between the two targets: it decides whether a video’s questions reach a worker that has already processed that video.

Prerequisites

• A Kubernetes cluster with the Dynamo platform installed.
• Eight available H200 GPUs and a ReadWriteMany model-cache volume.
• An NGC API key with access to the Cosmos3 model artifacts.

Run commands from the repository root:

export CONTEXT="<your-kubernetes-context>"
export NAMESPACE="<your-namespace>"
export NGC_API_KEY="<your-ngc-api-key>"
kubectl --context "$CONTEXT" create namespace "$NAMESPACE"
: "${NGC_API_KEY:?Set NGC_API_KEY to your NGC API key}"
kubectl --context "$CONTEXT" -n "$NAMESPACE" create secret generic ngc-api-secret \
  --from-literal=NGC_API_KEY="$NGC_API_KEY"

Cluster-specific settings

The manifests contain no credentials or node placement, and most clusters need no changes. Add a private Kustomize overlay only when you see one of these symptoms after deploying:

Both images are public on nvcr.io, so an image-pull Secret is only needed when the cluster pulls through a private registry mirror. In that case, create it for the mirror:

kubectl --context "$CONTEXT" -n "$NAMESPACE" create secret docker-registry \
  registry-pull-secret --docker-server=<your-registry-mirror> \
  --docker-username=<user> --docker-password=<password>

Copy the profile’s deploy.yaml into a directory outside the repository and add a kustomization.yaml next to it. The patches apply only to the Worker (spec.components[1]); keep only the ones you need:

apiVersion: kustomize.config.k8s.io/v1beta1
kind: Kustomization
resources:
  - deploy.yaml
patches:
  - target:
      group: nvidia.com
      kind: DynamoGraphDeployment
    patch: |-
      - op: add
        path: /spec/components/1/podTemplate/spec/imagePullSecrets
        value:
          - name: registry-pull-secret
      - op: add
        path: /spec/components/1/podTemplate/spec/containers/0/env/-
        value:
          name: NGC_API_KEY
          valueFrom:
            secretKeyRef:
              name: ngc-api-secret
              key: NGC_API_KEY
      - op: add
        path: /spec/components/1/podTemplate/spec/nodeSelector
        value:
          nvidia.com/gpu.product: NVIDIA-H200

Use your cluster’s GPU node label if it differs. In the deploy step, apply the overlay with apply -k <overlay-dir> instead of apply -f .../deploy.yaml.

Populate the model cache

The model-download Job and serving workers use the same pinned image and shared cache. If shared-model-cache already exists, keep it; PVC storage class and access mode fields are immutable.

For a new cache, first replace the placeholder storageClassName in model-cache.yaml with a ReadWriteMany-capable storage class. Then run:

kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f recipes/cosmos3-super-reasoner/model-cache/model-cache.yaml
kubectl --context "$CONTEXT" -n "$NAMESPACE" delete job \
  cosmos3-super-reasoner-model-download --ignore-not-found
kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f recipes/cosmos3-super-reasoner/model-cache/model-download.yaml
kubectl --context "$CONTEXT" -n "$NAMESPACE" wait \
  --for=condition=Complete job/cosmos3-super-reasoner-model-download \
  --timeout=7200s

Deploy

Choose one profile and deploy it. If you created an overlay in Cluster-specific settings, use apply -k instead of apply -f:

export PROFILE=round-robin # or: session-affinity
export DGD="cosmos3-super-reasoner-agg-h200-${PROFILE}"
kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f "recipes/cosmos3-super-reasoner/vllm/agg-h200-${PROFILE}/deploy.yaml"
kubectl --context "$CONTEXT" -n "$NAMESPACE" wait \
  --for=condition=Ready "dynamographdeployment/${DGD}" \
  --timeout=3600s

The round-robin manifest does not set a session-affinity flag. The session-affinity manifest sets a 300-second TTL.

Verify

Forward the frontend Service. The command keeps running:

kubectl --context "$CONTEXT" -n "$NAMESPACE" port-forward \
  "service/${DGD}-frontend" 8000:8000

In a second terminal, check that it serves the model:

curl -fsS http://localhost:8000/v1/models

The response lists nvidia/cosmos3-super-reasoner.

Smoke test

The smoke test generates one synthetic 30-second video with FFmpeg, exposes it to the workers through an in-cluster Service, and validates a non-empty multimodal completion:

kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f "recipes/cosmos3-super-reasoner/perf/profiles/${PROFILE}.yaml"
kubectl --context "$CONTEXT" -n "$NAMESPACE" delete job \
  cosmos3-super-reasoner-smoke --ignore-not-found
kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f recipes/cosmos3-super-reasoner/perf/smoke.yaml
kubectl --context "$CONTEXT" -n "$NAMESPACE" logs -f \
  job/cosmos3-super-reasoner-smoke

The final log line starts with Smoke test passed and includes the model’s description of the video.

Benchmark

The AIPerf workflow generates 200 synthetic 30-second, 2 FPS videos with FFmpeg before the measured sweep, so no external dataset or credentials are needed. It then sends 20 questions for each video at every concurrency level: 4,000 requests with exactly 128 output tokens each. Every request puts the video before the question, so all questions about one video share a prompt prefix. For session affinity, AIPerf sends one X-Dynamo-Session-ID value, generated per video, on all 20 questions about that video; pure round robin sends no session-affinity header.

kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f "recipes/cosmos3-super-reasoner/perf/profiles/${PROFILE}.yaml"
kubectl --context "$CONTEXT" -n "$NAMESPACE" delete job \
  cosmos3-super-reasoner-perf --ignore-not-found
kubectl --context "$CONTEXT" -n "$NAMESPACE" apply \
  -f recipes/cosmos3-super-reasoner/perf/perf.yaml
kubectl --context "$CONTEXT" -n "$NAMESPACE" logs -f \
  job/cosmos3-super-reasoner-perf

On eight H200 workers, the full sweep takes about 2.5 hours for round robin and about 2 hours for session affinity. Each concurrency level prints its AIPerf summary to the log when it finishes.

Measured performance

These are single-run AIPerf measurements from the eight-H200 deployments in this recipe. Each row represents 4,000 successful requests with no errors. Both profiles used the same 200 synthetic videos, 20 questions per video, and 128 output tokens.

At concurrency 16, session affinity produced 2,276 output tokens/s versus 1,619 for round robin. Request-latency p50 fell from 1,140 ms to 919 ms, and TTFT p90 fell from 757 ms to 47 ms. Each worker processes a video the first time it sees it and reuses it for later questions. Round robin spreads a video’s 20 questions across up to eight workers, so the video is processed on each of them, while session affinity processes it once.

The complete TTFT, request-latency, ITL, and throughput sweep is available in the benchmark README.

Source

• Recipe overview
• Round-robin deployment
• Session-affinity deployment
• AIPerf benchmark
• Video smoke test