NVIDIA Dynamo, fleet-wide

In Modelplane v0.4 you can now choose the serving stack each Modelplane cluster runs. The new Dynamo serving stack uses NVIDIA Dynamo. Grove and the KAI Scheduler place a multi-node engine as a gang, and ModelExpress moves model weights GPU to GPU between replicas.

The ModelDeployment an ML team writes stays the same. The same manifest runs on either stack. Which stack a cluster runs is a platform decision, made per cluster, so a fleet can run both at once.

A serving stack owns one cluster#

Modelplane operates a fleet. It provisions clusters and node pools, schedules each model replica onto hardware that fits, stages weights once per cluster, and fronts the whole fleet with one OpenAI-compatible endpoint. It isn't a serving layer itself.

A serving stack owns what happens inside one cluster. It places a multi-node engine's pods and gets the model's weights into GPU memory. Dynamo does both, and it reaches into territory Modelplane's current "standard" stack doesn't, like gang scheduling, P2P weight transfer, and keeping weights resident in GPU memory across an engine crash.

Opting a cluster in#

apiVersion: modelplane.ai/v1alpha1
kind: InferenceCluster
metadata:
  name: eks-h200-us-east
spec:
  # Standard (the default) or Dynamo. Immutable.
  stack: Dynamo
  cluster:
    source: EKS
    eks:
      region: us-east-1
  nodePools:
  - name: gpu
    className: eks-h200-8x
    nodeCount: 2

On a Dynamo cluster Modelplane installs Grove, the KAI Scheduler, and one ModelExpress server. On a Standard cluster it installs the LeaderWorkerSet controller. Everything else about a cluster, from how it fronts requests to how it stages model weights, is the same on both.

The choice is immutable, which makes adoption incremental. A platform team stands up a Dynamo cluster next to the ones it already runs and moves deployments over cluster by cluster.

Gang scheduling with Grove and the KAI Scheduler#

Gang scheduling makes the most of the GPU time you're paying for. A multi-node engine is a gang. Its leader and workers are useless apart. Schedule those pods one at a time and a gang can half-land, holding GPUs while serving nothing, waiting for nodes that may not be free for a while. KAI places the whole gang or none of it.

Modelplane composes an engine onto whichever stack its cluster runs. A Standalone engine is a Deployment on both. A Leader and Worker gang is a LeaderWorkerSet on Standard, and on Dynamo a Grove PodCliqueSet with a leader clique and a worker clique, scheduled by KAI.

So a serving stack has to run two pod specs with distinct commands, and give a worker a way to find its leader. Grove does both.

Weight transfer with ModelExpress#

Loading weights is slow. Each replica reads the model from storage before it can serve a token, and several replicas scaling up together compete for reads from the same storage. ModelExpress makes that one read rather than one per replica.

A Dynamo cluster runs one ModelExpress server. It brokers which replica holds a model in GPU memory, and never touches the weight bytes itself. The first replica loads from the cache volume and publishes itself as a source, and later replicas pull the weights from a peer's GPU over RDMA, across a fast fabric like EFA on EKS. A replica that finds no peer, or no fabric to reach one over, reads the cache volume instead, so size and keep the cache for every replica on either stack.

The same manifest on either stack#

An ML team writes one container named engine with its image, command, and args, and what they write is what runs. A ModelDeployment describes that engine and says nothing about the stack underneath it.

Here's a 480B model across two nodes, tensor-parallel within each node and pipeline-parallel across them, that also opts into ModelExpress. $(MODELPLANE_LEADER_ADDRESS) is the address the leader is reachable at, and it resolves on both stacks:

apiVersion: modelplane.ai/v1alpha1
kind: ModelDeployment
metadata:
  name: qwen3-coder
  namespace: ml-team
spec:
  replicas: 1
  template:
    spec:
      modelCacheRef:
        name: qwen3-coder
      engines:
      - name: qwen3-coder
        members:
        - role: Leader
          nodeSelector:
            devices:
            # Eight GPUs per node, each with at least 120Gi of memory.
            - name: gpu
              count: 8
              selectors:
              - cel: |
                  device.capacity["gpu.nvidia.com"].memory.compareTo(quantity("120Gi")) >= 0
          template:
            spec:
              containers:
              - name: engine
                image: vllm/vllm-openai:v0.23.0
                command:
                - /bin/sh
                - -c
                - >-
                  pip install --index-url https://pypi.nvidia.com modelexpress &&
                  exec vllm serve Qwen/Qwen3-Coder-480B-A35B-Instruct
                  --served-model-name=qwen3-coder
                  --load-format modelexpress
                  --tensor-parallel-size=8
                  --pipeline-parallel-size=2
                  --distributed-executor-backend=mp
                  --nnodes=2 --node-rank=0
                  --master-addr=$(MODELPLANE_LEADER_ADDRESS)
                  --max-model-len=32768
                  --port=8000
        - role: Worker
          worker:
            nodes: 1
          # nodeSelector is the same as the leader's. Omitted for brevity.
          template:
            spec:
              containers:
              - name: engine
                image: vllm/vllm-openai:v0.23.0
                command:
                - /bin/sh
                - -c
                - >-
                  pip install --index-url https://pypi.nvidia.com modelexpress &&
                  exec vllm serve Qwen/Qwen3-Coder-480B-A35B-Instruct
                  --served-model-name=qwen3-coder
                  --load-format modelexpress
                  --tensor-parallel-size=8
                  --pipeline-parallel-size=2
                  --distributed-executor-backend=mp
                  --nnodes=2 --node-rank=1
                  --master-addr=$(MODELPLANE_LEADER_ADDRESS)
                  --headless
                  --max-model-len=32768

The modelCacheRef names a ModelCache, which stages a model's weights once per cluster on shared storage. Both members name the model by its Hugging Face repo id, and Modelplane points the engine's HF_HUB_CACHE at the mount, so the engine resolves that repo id against the staged snapshot instead of downloading it.

Modelplane doesn't add a load format of its own, so --load-format modelexpress above is the ML team's opt-in rather than something the stack injects. That's what keeps the manifest portable. Run it on a Standard cluster, where nothing runs a ModelExpress server, and the engine reads the cache volume.

What's next#

The end state is the same cluster opt-in composing a full DynamoGraphDeployment, so a fleet gets Dynamo's frontend and router while the API an ML team writes stays what it is. NVIDIA has the upstream work in flight. A component's worker pod spec needs to be able to differ from its leader's, and the operator needs a switch to leave the user's command alone rather than generating launch flags, both of which are in dynamo#12696. The engine also runs today as a Dynamo runtime image rather than the stock vllm serve an ML team writes everywhere else, which dynamo#10835 addresses by moving the runtime wrapper into a sidecar.

Try it#

The getting-started guide covers standing up a fleet, and how it works covers what a serving stack installs. Modelplane is Apache 2.0 and moving fast at github.com/modelplaneai/modelplane, and questions are welcome in Slack.