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NVIDIA Nemotron-3-Nano-30B-A3B
serving recipe.

NVIDIA Nemotron-3-Nano-30B-A3B is a 30B total, 3B active mixture of experts model with a 256K tokens context window. vLLM publishes a tested serving recipe for it, and Wingsmith reads that recipe when you register the weights.

Size
30B total, 3B activeMixture of experts
Context
256K tokensAs the recipe states it.
Memory floor
35 GBSmallest published, whole replica.
vLLM
0.11.2 or laterMinimum version in the recipe.

01 / Memory

Precisions and memory floors.

Each variant is a precision vLLM tested, sometimes published as its own repository.

VariantPrecisionMemory floorWeights
defaultBF1672 GBnvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16
fp8FP835 GBnvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-FP8

Floors are the minimum the vLLM recipe publishes across the whole replica, from vLLM's own runs. The control plane compares them with its own sizing from the model config.

02 / Parallelism

Layouts the recipe supports.

  • Tensor ParallelFits inside one node's interconnect. single_node_tp
  • Multi-Node Tensor ParallelSpans nodes. multi_node_tp
  • Multi-Node TP + Pipeline ParallelSpans nodes. multi_node_tp_pp
  • Multi-Node Tensor + Expert ParallelSpans nodes. multi_node_tep
  • Multi-Node Data + Expert ParallelSpans nodes. multi_node_dep

03 / Hardware

Where vLLM ran it.

Node shapes the recipe reports an end-to-end run on.

  • H100 · 8 GPUs
  • H200 · 8 GPUs
  • Arc Pro B60 · 4 GPUs
  • Arc Pro B70 · 4 GPUs

Wingsmith runs models through NVIDIA Dynamo, so it serves on NVIDIA GPUs. Other vendors are listed as the recipe states them.

04 / Flags

The tested baseline.

Base flags, then the default variant, then the features that are on by default, as the recipe composes them.

vllm serve nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 \
  --trust-remote-code \
  --async-scheduling \
  --kv-cache-dtype auto \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser-plugin nano_v3_reasoning_parser.py \
  --reasoning-parser nano_v3
Single-node baseline. Multi-node layouts add flags per strategy and role.

Features

  • Tool callingOn by defaultQwen3 Coder tool-call parser with automatic tool choice
  • ReasoningOn by defaultCustom Nano v3 reasoning parser (download plugin: nano_v3_reasoning_parser.py)

05 / Serve

Serve it with Wingsmith.

From a slug to an OpenAI-compatible endpoint, across your clusters.

  1. Register the weights

    In the console, or through the API. Resolving a slug proposes an artifact and writes nothing.

    curl "http://localhost:8000/v1/artifacts/resolve?source=hf:nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16"
    Against the local control plane from the quickstart, where admin auth is off.

  2. Read the draft

    The draft carries this recipe as upstream evidence: the tested flags, the precision and memory floor, the parallelism layouts, and the hardware vLLM verified, with links back to the recipe. Nothing from it is applied without you, and the draft warns when the plane's own sizing and the published floor differ by a quarter or more.

  3. Create the workload

    Choose the engine, device shape and scale. The plane picks the clusters, and each cluster's operator renders the deployment as a Dynamo graph.

  4. Give it an endpoint

    One model name for clients, routed by the gateway to deployments on every cluster serving it.

Run the control plane locally →

Questions

Common questions.

  • How much GPU memory does NVIDIA Nemotron-3-Nano-30B-A3B need?The vLLM recipe publishes a minimum of 35 GB across the whole replica for its FP8 variant; BF16 72 GB. That is vLLM's own measurement, not a per-GPU figure.
  • Which parallelism layouts does the NVIDIA Nemotron-3-Nano-30B-A3B recipe support?Tensor Parallel, Multi-Node Tensor Parallel, Multi-Node TP + Pipeline Parallel, Multi-Node Tensor + Expert Parallel and Multi-Node Data + Expert Parallel.
  • Can Wingsmith serve NVIDIA Nemotron-3-Nano-30B-A3B?Wingsmith serves models through NVIDIA Dynamo on clusters with NVIDIA GPUs. Register nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 and the plane attaches this vLLM recipe to the draft as evidence: its tested flags, precision and memory floor, for you to review rather than applied on your behalf.

From the vLLM recipe for nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 ↗ (snapshot at 4bcc189), Apache 2.0.

Every model

Status

Open source,
under Apache 2.0.

Wingsmith is pre-release. The first public release, and everything after it, will be under Apache 2.0.

View on GitHubRead the docs Run it locally

Built and not built

Built

Multi-cluster placement and routing, Dynamo deployments, device inventory, endpoints and quotas, the console, the CLI, and three measurement tools.

Not built

Published images and charts, per-user roles and SSO, batch and training workloads, and a Helm chart for the control plane.

Pre-release. No tagged release, images, or charts yet, and APIs and protocols can change.