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NVIDIA Nemotron-Nano-9B-v2
serving recipe.

NVIDIA Nemotron-Nano-9B-v2 is a 9B dense model with a 128K tokens context window. vLLM publishes a tested serving recipe for it, and Wingsmith reads that recipe when you register the weights.

Size
9BDense
Context
128K tokensAs the recipe states it.
Memory floor
6 GBSmallest published, whole replica.
vLLM
0.10.1 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
defaultBF1622 GBnvidia/NVIDIA-Nemotron-Nano-9B-v2
fp8FP811 GBnvidia/NVIDIA-Nemotron-Nano-9B-v2-FP8
nvfp4NVFP46 GBnvidia/NVIDIA-Nemotron-Nano-9B-v2-NVFP4
baseBF1622 GBnvidia/NVIDIA-Nemotron-Nano-9B-v2-Base
japaneseBF1622 GBnvidia/NVIDIA-Nemotron-Nano-9B-v2-Japanese

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

03 / Hardware

Where vLLM ran it.

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

  • B200 · 8 GPUs
  • H100 · 8 GPUs
  • H200 · 8 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-Nano-9B-v2 \
  --trust-remote-code \
  --enable-auto-tool-choice \
  --tool-call-parser-plugin nemotron_toolcall_parser_no_streaming.py \
  --tool-call-parser nemotron_json
Single-node baseline. Multi-node layouts add flags per strategy and role.

Features

  • Tool callingOn by defaultCustom Nemotron tool-call parser plugin (download: nemotron_toolcall_parser_no_streaming.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-Nano-9B-v2"
    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-Nano-9B-v2 need?The vLLM recipe publishes a minimum of 6 GB across the whole replica for its NVFP4 variant; BF16 22 GB, FP8 11 GB, BF16 22 GB, BF16 22 GB. That is vLLM's own measurement, not a per-GPU figure.
  • Which parallelism layouts does the NVIDIA Nemotron-Nano-9B-v2 recipe support?Tensor Parallel, Multi-Node Tensor Parallel and Multi-Node TP + Pipeline Parallel.
  • Can Wingsmith serve NVIDIA Nemotron-Nano-9B-v2?Wingsmith serves models through NVIDIA Dynamo on clusters with NVIDIA GPUs. Register nvidia/NVIDIA-Nemotron-Nano-9B-v2 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-Nano-9B-v2 ↗ (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.