Deploying a model with NVIDIA Dynamo and vLLM means putting Dynamo in charge of request routing and worker coordination while vLLM executes model inference. A minimal installation has one Dynamo frontend and at least one vLLM worker. For a single machine, start the frontend on port 8000, launch a worker with a compatible model, and send requests to the frontend’s OpenAI-compatible HTTP API. On Kubernetes, define the same relationship in a DynamoGraphDeployment (DGD), then add secrets, storage, GPU resources, and replicas as the workload requires.
What Dynamo and vLLM each do
NVIDIA Dynamo is the serving framework around an inference engine. vLLM is one supported engine; Dynamo can also work with SGLang and TensorRT-LLM. In this arrangement, Dynamo provides the frontend, service discovery, routing, and deployment model, while vLLM loads the model and generates tokens.
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The smallest useful topology contains two roles:
- Frontend: accepts client requests and routes them to available workers. NVIDIA’s command-line walkthrough exposes an OpenAI-compatible HTTP endpoint on port 8000.
- Worker: runs the vLLM backend, loads model weights, manages GPU memory, and registers with the frontend.
Adding more workers increases serving capacity. Giving a worker more GPUs allows one model instance to span devices through parallelism. Separating prefill and decode workers creates a disaggregated deployment in which each phase can be sized independently.
NVIDIA documents Dynamo for local machines, Kubernetes clusters, and Slurm environments. The commands, resource ownership, networking, and failure handling differ between those environments, so choose one path rather than mixing setup instructions from another.
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Choose an environment and serving topology
| Decision | Use it when | What you must manage |
|---|---|---|
| Local or single node | You are validating a model or serving a small, self-managed workload. | Processes, GPU visibility, local model cache, and restarts. |
| Kubernetes | You need declarative resources, scheduling, replicas, cluster networking, and operator-managed workloads. | A DGD, container images, GPU requests, secrets, storage, and cluster observability. |
| Slurm | Your organization already schedules accelerators through Slurm. | Jobs, allocations, node placement, and process startup through the site’s Slurm policy. |
| Aggregated serving | One worker can perform both prefill and decode within the latency and memory target. | A single worker pool and one parallelism plan. |
| Disaggregated serving | Prefill and decode need different GPU counts, scaling rates, or latency objectives. | Separate worker pools, routing between phases, and independent capacity planning. |
| Multinode serving | A worker must span Kubernetes nodes because the model or target throughput exceeds one node. | Cross-node networking, placement, collective communication, and a topology validated for the chosen hardware. |
Start with aggregated serving unless measurements or model size justify the extra operational complexity of disaggregation. Multinode orchestration is a placement and networking decision, not merely a larger replica count.
Check versions, hardware, and model compatibility first
NVIDIA’s documentation index listed Dynamo v1.5.1 as the latest release when checked on October 13, 2026. Several implementation pages use the dev documentation path, so verify commands, container tags, and manifest fields against the release you install rather than copying a development example unchanged.
Confirm the backend and model
Choose vLLM only after checking that the selected vLLM and Dynamo releases support the model architecture, tokenizer, attention features, and quantization mode. NVIDIA describes vLLM as having broad model coverage and a mature feature set, but that is not a guarantee for every model revision or accelerator.
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Inventory accelerator memory
Record the GPU type, count per node, usable VRAM, interconnect, and whether the cluster also contains AMD GPUs or Intel XPUs. Dynamo documents support across NVIDIA and AMD GPUs and Intel XPUs, but the backend’s model and quantization support still determines what can actually run.
Use NVIDIA’s sizing relationship before selecting tensor parallelism:
weights + KV cache + overhead ≤ (parallelism size) × (VRAM per GPU)
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Prepare access to model weights
For a public model, workers can download weights at startup if network access and the required cache directory are available. Gated or rate-limited Hugging Face models require an access token stored in a Kubernetes Secret in the target namespace. Do not bake the token into an image or a DGD command line.
Run a single-node deployment with the CLI
The local route is useful for validating compatibility before introducing Kubernetes scheduling. Keep the frontend and worker processes in the same environment initially so registration and networking are easy to inspect.
- Install a release-matched Dynamo and vLLM environment. Use the installation method and package versions for the Dynamo release you selected. Confirm that the vLLM build sees the intended accelerator and that the model’s dependencies are present.
- Expose the frontend. Start the documented frontend process on port 8000:
python -m dynamo.frontend --http-port 8000
Keep this process running and note the host address that clients will use. - Start a vLLM worker. Launch the release’s Dynamo vLLM worker with the model identifier and the GPU visibility or parallelism settings chosen during sizing. NVIDIA’s examples use the
dynamo.vllmworker and a Hugging Face model identifier. Because launcher arguments can change between releases, use the worker command shipped with your installed version; for the walkthrough model, the identifier isQwen/Qwen3-32B. - Watch registration. The worker must register with the frontend before it can receive traffic. Check the worker log for successful backend initialization and registration, and check the frontend log for a discovered worker.
- Send an API request. Point an OpenAI-compatible client at the frontend on port 8000. Use the request path and payload format documented for your Dynamo release (chat-completions and completion-style paths can differ by version), and set the model field to the identifier loaded by the worker.
- Validate a small request first. Use a short prompt and low output limit. Confirm a response, then test longer context and concurrent requests while watching VRAM, latency, and worker errors.
- Increase capacity deliberately. Add worker replicas for more independent request capacity, or increase the worker’s tensor-parallel size when one model instance does not fit on one GPU. Recalculate memory before changing either setting.
If the frontend is reachable but requests fail, distinguish routing from inference: a missing worker registration is a control-plane problem; an out-of-memory error after registration is a model, context, or parallelism problem.
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A DynamoGraphDeployment (DGD) is the Kubernetes entry point for serving a model with Dynamo. It describes the graph of components—normally a frontend and one or more workers—along with images, commands, model arguments, environment variables, and resource requests.
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Create the DGD from a template or recipe
NVIDIA documents three practical approaches: use a supplied recipe, use a vLLM deployment template, or write the DGD directly. Templates are the fastest way to establish a known-good component layout; a hand-written DGD is useful when storage, environment variables, or topology differ from the template.
- Install the Dynamo Kubernetes components. Follow the operator and CRD installation for the exact release. Confirm that the cluster recognizes the DGD resource before creating workloads.
- Select the worker image. Use an image tag compatible with the installed Dynamo and vLLM versions. The image must contain the worker entry point and any model-specific runtime dependencies.
- Define the frontend component. Set its container image, service exposure, and HTTP port. The CLI guide uses port 8000; preserve that port consistently in the service and client configuration.
- Define the worker component. Set the
dynamo.vllmcommand or template equivalent, the model identifier, GPU resource requests, parallelism arguments, environment variables, and replica count. - Apply the manifest. Save the release-validated resource as
dgd.yamland runkubectl apply -f dgd.yamlin the namespace where the operator and secrets are installed. - Check readiness in order. Verify that pods are scheduled, GPUs are allocated, workers finish model initialization, workers register with the frontend, and the frontend service has an endpoint reachable by your client.
Do not assume that a pod in Running state is ready to serve. Weight downloads and engine initialization can take substantially longer than container startup.
Set GPU and storage resources
Request the number of GPUs required by the worker’s parallelism plan. A worker configured for multi-GPU tensor parallelism must be scheduled where those devices are available together; a replica count does not substitute for the GPUs needed by one model instance.
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By default, each worker may download model weights during startup. With large models or multiple replicas, that can create long rollouts and trigger registry or Hugging Face rate limits. Mount shared storage or a node-local cache strategy that your cluster supports, then configure the worker to use the cache. Ensure the cache has enough capacity for the full model, tokenizer files, and temporary artifacts.
Provide gated-model credentials safely
Create a Kubernetes Secret containing the model access token in the target namespace, then reference it from the worker’s environment through the DGD. Scope the secret to the service account or namespace that needs it, rotate it according to your security policy, and prevent it from appearing in pod logs or rendered manifests.
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When to increase tensor parallelism
Increase tensor parallelism when a single model instance cannot satisfy the memory inequality or when the target latency requires more compute per request. Recheck architecture, quantization, GPU interconnect, and backend support after every change. More GPUs can reduce per-device weight pressure, but communication overhead and placement constraints may reduce the benefit.
When to add replicas
Add replicas when one worker fits comfortably but queueing or throughput is inadequate. Replicas increase independent serving capacity and can improve availability, although each replica may download or cache a separate copy of the weights unless shared storage is configured.
When to disaggregate
Use separate prefill and decode pools when prompt processing and token generation have materially different resource needs. This lets you scale the pools independently, but it adds routing, capacity-balancing, and observability requirements. Measure request latency and queue behavior before adopting it.
NVIDIA points to AIConfigurator as an option for evaluating parallelism and replica configurations against hardware and latency targets. Treat its output as planning guidance and validate the resulting configuration with your own workload; no universal performance number applies across models and accelerators.
Operate and troubleshoot the deployment
| Symptom | Likely cause | Checks and corrective action |
|---|---|---|
| Frontend is up but no responses arrive | No worker registration or incorrect service routing. | Inspect frontend and worker logs, verify the worker can resolve the frontend, and confirm the service port is 8000 where configured. |
| Worker exits during startup | Unsupported architecture, quantization incompatibility, missing dependency, or insufficient VRAM. | Check the first backend error, test the model with the release-matched vLLM build, and recalculate weights, KV cache, and overhead. |
| Pods remain pending | Insufficient GPUs, incompatible node labels, or a multi-GPU worker that cannot be placed together. | Inspect scheduler events, GPU allocatable capacity, node selectors, and parallelism requirements. |
| Every rollout downloads weights | No shared cache or cache path is not writable. | Mount shared or node-local storage, configure the cache path, and verify permissions before increasing replicas. |
| Downloads are rejected or throttled | Missing token for a gated model or registry rate limiting. | Reference the namespace Secret, test authentication, and stage weights in shared storage where policy allows. |
| Requests run out of memory under load | KV-cache growth, longer contexts, higher concurrency, or too little headroom. | Reduce context or concurrency, increase parallelism, add replicas, or choose a quantization supported by the hardware. |
Collect frontend routing logs, worker engine logs, pod events, GPU utilization, VRAM consumption, request latency, queue depth, and error rates. Correlating these signals distinguishes scheduling failures from model-runtime failures and prevents blind changes to parallelism.
Keep the deployment reproducible
Pin the Dynamo, vLLM, container, and model revisions used in production. Record GPU type, driver, quantization, context limit, tensor-parallel size, replica count, cache location, and DGD manifest alongside the application configuration. On upgrade, deploy a separate namespace or canary worker, verify model loading and registration, then exercise representative prompts before moving traffic.
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Because NVIDIA’s detailed CLI and DGD implementation pages may track development documentation, re-check release-specific commands, image names, CRD fields, and worker arguments whenever you move from Dynamo v1.5.1 or install a newer version. The frontend/worker architecture and memory-sizing relationship remain useful planning concepts, but exact manifests are version-sensitive.
Quick Recap
Practical deployment checklist
- Environment selected: local, Kubernetes, or Slurm.
- Topology selected: aggregated, disaggregated, or multinode.
- Model architecture and quantization verified for the chosen vLLM, Dynamo, and accelerator versions.
- VRAM calculation includes weights, KV cache, and overhead.
- Frontend configured on the intended HTTP port, 8000 in the CLI example.
- At least one worker starts, loads the model, and registers successfully.
- Kubernetes DGDs request the required GPUs and expose the frontend service.
- Gated-model credentials are stored as namespace-scoped Secrets.
- Shared or node-local model caching is planned before adding replicas.
- Logs, GPU metrics, latency, queue depth, and errors are monitored.
- Commands and manifests are checked against the installed release rather than copied blindly from a development page.
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