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For the documented NVIDIA GPU path, pin Dynamo 1.5.1 with vLLM 0.28.0, then deploy a DynamoGraphDeployment (DGD) on Kubernetes 1.30 or later. Start with aggregated serving unless your traffic benefits from independent prefill and decode capacity. Add RDMA-capable networking, scheduling, storage, monitoring, and autoscaling only when the selected topology requires them.
This guide reflects NVIDIA documentation checked on October 13, 2026. Release selectors and manifests change, so verify the compatibility matrix, image tags, CRD versions, and installation prerequisites immediately before deployment.
What you are deploying
Dynamo is an open-source inference framework that can run vLLM, SGLang, or TensorRT-LLM backends. This procedure covers only NVIDIA GPUs on Kubernetes; requirements for AMD GPUs, Intel XPUs, Slurm, or local execution are different.
NVIDIA describes a DynamoGraphDeployment (DGD) as the Kubernetes entry point for serving a model. The resource declares the frontend, model workers, container images, resource requests and limits, model arguments, and the inference request flow. The Dynamo operator watches the DGD and creates or updates the corresponding workloads and services.
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A DGD is therefore the deployment artifact whether you copy an official recipe, generate one from a backend template, or author it yourself. The example model names, GPU limits, replica counts, and arguments in quickstarts are starting points, not evidence that they fit your model or service-level objective.
Pin compatible versions before touching the cluster
The current NVIDIA GPU Kubernetes quickstart pairs Dynamo 1.5.1 with vLLM 0.28.0. The same selector lists vLLM 0.26.0 with Dynamo 1.4.2. Keep the operator, runtime image, templates, and manifests from one compatible release family unless the support matrix explicitly permits a mix.
| Component | Documented pairing or requirement | How to use it |
|---|---|---|
| Dynamo | 1.5.1, latest stable in the selector checked October 13, 2026 | Pin the operator and platform images to this release. |
| vLLM | 0.28.0 for the Dynamo 1.5.1 NVIDIA GPU quickstart | Use the matching Dynamo vLLM runtime image or a documented compatible image. |
| Older documented pairing | vLLM 0.26.0 with Dynamo 1.4.2 | Use only when you intentionally deploy that older release family. |
| Kubernetes | 1.30 or later for the NVIDIA GPU quickstart | Confirm both control-plane and node compatibility. |
| kubectl | 1.30 or later | Use a client version supported by your cluster. |
| Helm | 3 or later | Required by the installation guidance for platform components. |
Record the chosen versions in infrastructure configuration rather than relying on floating tags. Recheck NVIDIA’s current selector and support matrix before applying manifests; development-path documentation and stable pages can diverge as releases change.
Cluster prerequisites for the NVIDIA Kubernetes path
- A Kubernetes 1.30-or-later cluster and kubectl 1.30-or-later access.
- Schedulable nodes with NVIDIA GPUs and the driver, device-plugin, and runtime integration required by your cluster.
- Helm 3 or later.
- Network access to the registries that host the selected Dynamo and vLLM images.
- A model-access credential when the model requires authenticated downloads; store it as a Kubernetes Secret rather than embedding it in a manifest.
- Enough CPU, memory, ephemeral storage, and GPU capacity for the model, worker parallelism, and expected replicas. The published examples do not establish a universal GPU count.
The installation guidance notes that the NVIDIA GPU Operator can install NVIDIA drivers. Do not enable it alongside provider-managed drivers unless you follow the instructions for that specific cluster; competing driver installation paths can leave nodes unschedulable or produce incompatible runtime libraries.
Install the platform in dependency order
Use NVIDIA’s installation sequence, adapting it to the topology you selected. The order matters because the operator and runtime depend on a functioning accelerator layer.
- Verify the cluster. Check the Kubernetes server and client versions, confirm that GPU nodes are Ready, and verify that a test pod can request an NVIDIA GPU. Resolve driver, device-plugin, taint, or runtime errors before installing Dynamo.
- Install accelerator support. Use one driver-management approach. If your environment uses the GPU Operator, let that component manage the driver lifecycle; if your provider already manages drivers, follow the provider-specific Dynamo prerequisites instead.
- Install the Dynamo Platform. Install the operator and its CRDs with Helm as specified by the pinned Dynamo release. Confirm that the operator deployment is Running and that its CRDs are established before creating a DGD.
- Install only topology-specific services. A single-node aggregated deployment may need no scheduler, RDMA fabric, or metrics stack beyond your existing platform. Add those components when the architecture section below calls for them.
- Check image and secret access. Ensure every node can pull the pinned runtime image and that the model credential Secret is in the namespace where the DGD will run.
Choose aggregated or disaggregated serving
Topology is a workload decision, not a mandatory upgrade path.
| Decision axis | Aggregated serving | Disaggregated serving |
|---|---|---|
| Worker role | One worker handles prompt processing (prefill) and token generation (decode). | Separate worker pools handle prefill and decode. |
| Best initial use | A model and traffic mix that fit together on one worker. | Workloads where prompt and generation phases need independent capacity or scaling. |
| Scaling | Scale complete workers. | Scale prefill and decode pools independently. |
| Network sensitivity | Normal service networking is often simpler. | KV-cache movement between pools makes network performance a first-class dependency. |
| Operations | Fewer components and simpler scheduling. | More workers, placement constraints, observability, and failure modes. |
| Evidence needed | Measure end-to-end latency and throughput under representative concurrency. | Measure phase-specific latency, transfer performance, and end-to-end behavior with the intended network. |
When aggregated serving is the right baseline
Choose aggregated serving when a worker can keep both prefill and decode busy without violating latency goals. It is usually the shortest path to a first production candidate because there is no separate transfer plane or independent worker-pool scheduler to operate. Validate the choice with your actual prompt-length distribution, output-token distribution, concurrency, model quantization, and GPU memory headroom.
When disaggregation is justified
Choose disaggregation when prompt processing and token generation have materially different demand patterns and independent pools let you place or scale them more effectively. Compare it against aggregation using measured time to first token, inter-token latency, completion latency, throughput, queueing, and failure recovery. NVIDIA’s documentation describes the topology options but does not establish a universal winner or a standard GPU count.
Production disaggregation also requires a network design that can move KV cache efficiently. NVIDIA identifies Network Operator and RDMA for production disaggregated inference and warns that TCP fallback can cause approximately 200–500× degradation in KV-cache transfer performance relative to the expected production networking path. This is NVIDIA’s published estimate, not an independently measured guarantee for every cluster.
Create the DynamoGraphDeployment
Start from a recipe that matches your backend, model family, GPU type, and topology. If no recipe fits, adapt the vLLM backend template or author the DGD directly. Keep the manifest in version control and review it like application code.
Fields your DGD must express
- Frontend: the service that accepts inference requests and routes them into the Dynamo graph.
- Worker image: the pinned Dynamo vLLM runtime image compatible with your selected release.
- Model arguments: model identifier, tokenizer or parallelism options, context settings, and any vLLM flags required by the model.
- GPU resources: requests and limits that match the worker’s placement and parallelism. Treat example values as placeholders, not sizing advice.
- Credentials: a reference to a Kubernetes Secret for models that require authenticated registry or model-hub access.
- Topology: aggregated worker configuration or distinct prefill and decode worker pools.
- Placement: node selectors, affinities, tolerations, and disruption policy appropriate to the GPU inventory.
Apply the DGD only after the operator and CRDs are healthy. A typical workflow is:
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dgd.yaml. - Run
kubectl apply -f dgd.yamlin the intended namespace. - Inspect the DGD and generated resources with
kubectl get dgd,kubectl describe dgd,kubectl get pods, andkubectl get services. - Follow events and worker logs until image pulls, model loading, and readiness probes complete.
Do not treat a Running pod as proof that the model is usable. A worker can be alive while still downloading weights, waiting for a credential, failing to initialize CUDA, or unable to join the intended graph.
Install conditional production dependencies
NVIDIA’s production guidance makes several dependencies architecture-dependent.
Grove and gang scheduling
NVIDIA lists Grove plus a gang scheduler for multinode or disaggregated inference. Gang scheduling helps ensure that a coordinated group of workers is admitted together instead of leaving a graph partially placed. A single-node aggregated deployment may not need these components; install them when your worker set spans nodes or requires coordinated startup.
Network Operator and RDMA
For production disaggregation, provide the Network Operator/RDMA path documented for your cluster. Verify that the intended interfaces, device resources, and permissions are visible inside the worker pods. If the graph silently falls back to TCP, treat the deployment as unsuitable for a latency-sensitive disaggregated workload until the network path is corrected and measured.
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Prometheus-compatible monitoring
Install kube-prometheus-stack when you use planner SLA mode or KEDA/HPA autoscaling, or connect Dynamo metrics to an existing Prometheus-compatible stack. Monitoring is useful for every topology, but this component is not a mandatory prerequisite for every smoke test.
Shared model storage
NVIDIA recommends shared model storage for models larger than 70B parameters or for deployments with many replicas. The goal is to avoid repeated downloads, startup delays, and Hugging Face rate limits. Treat 70B as documented guidance, not an immutable boundary: smaller models can also benefit when replicas restart frequently or egress is constrained.
Validate health before sending traffic
Validation should progress from infrastructure to application behavior.
- Operator health: confirm the Dynamo operator is Ready and its CRDs are established.
- Scheduling: confirm every expected frontend and worker pod is scheduled onto nodes with the required GPU resources.
- Readiness: wait for model loading and readiness probes; inspect events for failed pulls, missing Secrets, CUDA initialization errors, or out-of-memory failures.
- Frontend endpoint: query the frontend’s documented health endpoint. Dynamo exposes
/liveand/healthendpoints for Kubernetes probes; use the endpoint associated with the probe you are checking. - Representative request: send an OpenAI-compatible chat-completion request through the frontend service, using a prompt and generation limit representative of your application.
A generic request shape is:
curl http://<frontend-service>:8000/v1/chat/completions -H 'Content-Type: application/json' -d '{"model":"your-model-id","messages":[{"role":"user","content":"Reply with one sentence."}],"max_tokens":32}'Replace the service address, model identifier, and port with the values exposed by your DGD. A successful response proves request routing and basic model execution; it does not prove capacity, tail latency, autoscaling behavior, or an SLO.
Observe the system you actually deployed
Collect signals at the frontend, worker, operator, and cluster layers.
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- Request metrics: request rate, success and error counts, queueing, and request duration.
- Latency metrics: time to first token, inter-token latency, completion latency, and tail percentiles where exposed by your instrumentation.
- Routing metrics: selected worker, retries, rejected requests, and phase routing for disaggregated graphs.
- Worker and cache metrics: GPU utilization and memory, KV-cache usage, cache hits or evictions, batch size, and out-of-memory events.
- Operator metrics: reconciliation errors, rollout status, and resource creation failures.
- Logs: frontend access logs, worker startup/model-loading logs, scheduler events, and network-plugin errors.
- Traces: export distributed traces through OTLP and correlate frontend spans with worker and transfer spans.
- Health endpoints: alert on failed
/liveand/healthprobes, but distinguish liveness from readiness in incident response.
Keep model-load time, prompt lengths, output lengths, concurrency, GPU type, topology, and software versions with every benchmark record. Without those dimensions, a throughput number cannot be compared meaningfully.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Load-test for your workload, not a sample prompt
Run a staged test after the smoke test and before setting replica counts or latency commitments.
- Build a traffic profile. Include the real model, prompt-length distribution, expected output lengths, request arrival pattern, streaming behavior, concurrency, and cancellation rate.
- Establish a low-concurrency baseline. Measure startup completion, first-token latency, inter-token latency, total completion latency, error rate, and GPU memory.
- Increase concurrency gradually. Find the point where queueing, tail latency, cache pressure, or error rates become unacceptable.
- Test failures. Restart a worker, drain a GPU node, expire a model credential in a controlled environment, and verify that the frontend reports failures and recovers as designed.
- Compare topologies. For disaggregation, test prefill-heavy, decode-heavy, and mixed traffic while recording KV-cache transfer behavior and network utilization.
- Test scaling. Exercise the planner or KEDA/HPA policy if enabled, including scale-up delay, scale-down stability, and model-loading impact.
- Set capacity from measurements. Choose replicas and headroom from observed SLO margins, not from the quickstart’s example resources.
No universal throughput, latency, cost, or GPU-sizing result can be inferred without a specified model, hardware inventory, traffic mix, and objective.
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Troubleshoot common production failures
Pods remain Pending
Inspect events for insufficient GPU resources, taints without matching tolerations, node selectors that match no nodes, or gang-scheduling constraints. Confirm that the requested GPU resource name matches the device plugin installed on your nodes.
ImagePullBackOff or model-download failures
Check registry credentials, image tag spelling, outbound network policy, and the model-access Secret in the correct namespace. For many replicas or models above NVIDIA’s documented 70B shared-storage threshold, move weights to shared storage to reduce repeated downloads and rate-limit exposure.
CUDA initialization or out-of-memory errors
Verify driver/runtime compatibility, GPU visibility inside the container, model parallelism, context-length settings, and the worker’s memory budget. Do not increase replicas blindly; a single worker may still be configured with an incompatible model or parallelism layout.
Health is live but requests fail
Check readiness, frontend routing, worker logs, model identifier spelling, and the OpenAI-compatible API path exposed by your service. Liveness only indicates that a process is running; it does not confirm that weights loaded or that a worker can generate tokens.
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Confirm that the intended RDMA path is active and that the graph is not using TCP fallback. Inspect KV-cache transfer metrics, network errors, worker placement, and prefill/decode queue imbalance before changing model or replica settings.
Autoscaling behaves erratically
Verify that the metrics stack is installed and scraping the relevant endpoints, that planner SLA or KEDA/HPA thresholds match the measured workload, and that model-loading time is included in scaling decisions. Test scale-down under sustained low traffic so that workers do not churn.
Production readiness checklist
- Documented Dynamo and vLLM versions are mutually supported and pinned.
- Kubernetes and kubectl meet the 1.30-or-later quickstart requirement, and Helm is version 3 or later.
- NVIDIA GPU nodes pass a device and runtime verification.
- Driver ownership is unambiguous; GPU Operator and provider-managed drivers are not conflicting.
- The DGD is version-controlled, reviewed, and matched to the model, backend, GPU, and topology.
- Aggregated versus disaggregated serving was selected from measured workload characteristics.
- Grove and gang scheduling are installed for multinode or disaggregated requirements when applicable.
- Network Operator/RDMA is configured and verified for production disaggregation.
- Shared model storage is considered for models over 70B or deployments with many replicas.
- Secrets, image pulls, model downloads, and namespace permissions are tested.
/live,/health, metrics, logs, and OTLP traces are integrated with operations.- Representative load tests establish capacity, tail latency, failure recovery, and scaling behavior.
The Bottom Line
Deploy the pinned Dynamo 1.5.1 and vLLM 0.28.0 pairing on a Kubernetes 1.30-or-later NVIDIA GPU cluster, use a DGD as the controlled deployment artifact, begin with aggregated serving unless measurements justify disaggregation, and treat RDMA, scheduling, storage, observability, and load testing as workload-dependent production decisions.
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