> This page is for version v1.1.0.
> For other versions, use one of these documentation indexes:
> - Latest (v1.5.1) (default): https://docs.nvidia.com/dynamo/latest/llms.txt
> - dev: https://docs.nvidia.com/dynamo/dev/llms.txt
> - v1.5.1: https://docs.nvidia.com/dynamo/v1.5.1/llms.txt
> - v1.5.0: https://docs.nvidia.com/dynamo/v1.5.0/llms.txt
> - v1.4.2: https://docs.nvidia.com/dynamo/v1.4.2/llms.txt
> - v1.4.1: https://docs.nvidia.com/dynamo/v1.4.1/llms.txt
> - v1.4.0: https://docs.nvidia.com/dynamo/v1.4.0/llms.txt
> - v1.3.0: https://docs.nvidia.com/dynamo/v1.3.0/llms.txt
> - v1.2.1: https://docs.nvidia.com/dynamo/v1.2.1/llms.txt
> - v1.2.0: https://docs.nvidia.com/dynamo/v1.2.0/llms.txt
> - v1.1.1: https://docs.nvidia.com/dynamo/v1.1.1/llms.txt
> - v1.1.0: https://docs.nvidia.com/dynamo/v1.1.0/llms.txt
> - v1.0.2: https://docs.nvidia.com/dynamo/v1.0.2/llms.txt
> - v1.0.1: https://docs.nvidia.com/dynamo/v1.0.1/llms.txt
> - v1.0.0: https://docs.nvidia.com/dynamo/v1.0.0/llms.txt
> - v0.9.1: https://docs.nvidia.com/dynamo/v-0-9-1/llms.txt
> - v0.9.0: https://docs.nvidia.com/dynamo/v-0-9-0/llms.txt
> - v0.8.1: https://docs.nvidia.com/dynamo/v-0-8-1/llms.txt
> - v0.8.0: https://docs.nvidia.com/dynamo/v-0-8-0/llms.txt
> - v0.7.1: https://docs.nvidia.com/dynamo/v-0-7-1/llms.txt
> - v0.7.0: https://docs.nvidia.com/dynamo/v-0-7-0/llms.txt

> For clean Markdown content of this page, append .md to this URL. For the complete documentation index, see https://docs.nvidia.com/dynamo/llms.txt. For full content including API reference and SDK examples, see https://docs.nvidia.com/dynamo/llms-full.txt.

# Multinode Examples

For general TensorRT-LLM features and engine configuration, see the
[Reference Guide](/dynamo/v1.1.0/backends/tensor-rt-llm/reference-guide).

## Recommended Path

For multinode TensorRT-LLM deployments, start from the checked-in Kubernetes
recipes under [`recipes/`](../../../../recipes/README.md). Those manifests are
the supported entrypoints for launching multi-node workers, frontend services,
and related routing components.

The main TRT-LLM recipe entrypoints are:

- [DeepSeek-R1 WideEP on GB200](../../../../recipes/deepseek-r1/trtllm/disagg/wide_ep/gb200/deploy.yaml)
- [Qwen3-235B-A22B-FP8 aggregated](../../../../recipes/qwen3-235b-a22b-fp8/trtllm/agg/deploy.yaml)
- [Qwen3-235B-A22B-FP8 disaggregated](../../../../recipes/qwen3-235b-a22b-fp8/trtllm/disagg/deploy.yaml)
- [Qwen3-32B-FP8 aggregated](../../../../recipes/qwen3-32b-fp8/trtllm/agg/deploy.yaml)
- [Qwen3-32B-FP8 disaggregated](../../../../recipes/qwen3-32b-fp8/trtllm/disagg/deploy.yaml)
- [GPT-OSS-120B aggregated](../../../../recipes/gpt-oss-120b/trtllm/agg/deploy.yaml)
- [GPT-OSS-120B disaggregated](../../../../recipes/gpt-oss-120b/trtllm/disagg/deploy.yaml)
- [Nemotron-3-Super-FP8 disaggregated](../../../../recipes/nemotron-3-super-fp8/trtllm/disagg/deploy.yaml)

For model-level setup, prerequisites, and hardware notes, use the recipe
README files:

- [DeepSeek-R1 recipes](../../../../recipes/deepseek-r1/README.md)
- [Qwen3-235B-A22B-FP8 recipes](../../../../recipes/qwen3-235b-a22b-fp8/README.md)
- [Qwen3-32B-FP8 recipes](../../../../recipes/qwen3-32b-fp8/README.md)
- [GPT-OSS-120B recipes](../../../../recipes/gpt-oss-120b/README.md)
- [Kimi-K2.5 recipes](../../../../recipes/kimi-k2.5/README.md)

## Quick Start

At a high level, the Kubernetes workflow is:

1. Install the Dynamo platform on Kubernetes. See the
   [Kubernetes Deployment Guide](/dynamo/v1.1.0/getting-started/kubernetes-deployment).
2. Create a namespace and any required secrets such as a Hugging Face token.
3. Apply the recipe's model cache and model download manifests when the recipe
   includes them.
4. Apply the recipe's `deploy.yaml`.
5. Port-forward the frontend service and send test requests to `/v1/models` or
   `/v1/chat/completions`.

Example flow:

```bash
export NAMESPACE=dynamo-demo
kubectl create namespace ${NAMESPACE}

kubectl create secret generic hf-token-secret \
  --from-literal=HF_TOKEN="your-token-here" \
  -n ${NAMESPACE}

# Example: deploy DeepSeek-R1 TRT-LLM WideEP on GB200.
kubectl apply -f recipes/deepseek-r1/model-cache/model-cache.yaml -n ${NAMESPACE}
kubectl apply -f recipes/deepseek-r1/model-cache/model-download.yaml -n ${NAMESPACE}
kubectl wait --for=condition=Complete job/model-download -n ${NAMESPACE} --timeout=7200s
kubectl apply -f recipes/deepseek-r1/trtllm/disagg/wide_ep/gb200/deploy.yaml -n ${NAMESPACE}
```

After the deployment is ready, port-forward the frontend service named by the
recipe and send a test request:

```bash
kubectl port-forward svc/<frontend-service> 8000:8000 -n ${NAMESPACE}

curl http://localhost:8000/v1/models
```

## Notes

- The TRT-LLM engine config files used by launch and deploy flows live under
  [`examples/backends/trtllm/engine_configs/`](../../../../examples/backends/trtllm/engine_configs/README.md).
- If you need to customize model parallelism, replica counts, or routing mode,
  edit the recipe-local manifest rather than introducing a separate scheduler-specific guide.
- For the current catalog of supported recipes, see [recipes/README.md](../../../../recipes/README.md).