Model reference · open weights

NVIDIA-Nemotron-3.5-Super-VL

LLMs nvidia Vision + text · MoE 5 builds Its own licence terms 142 dl/mo

NVIDIA-Nemotron-3.5-Super-VL is an open-weight language model from NVIDIA. NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 (NVFP4) weighs 75.0 GB; the smallest configuration that runs it is A100 80 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • NVIDIA-Nemotron-3.5-Super-VL is a vision-language model by NVIDIA designed for reasoning, coding, tool use, and understanding documents and video.
  • It features a Mamba-2 and Transformer hybrid architecture with 120B total parameters and 12B active parameters, supporting a context length of up to 1M tokens.
  • The model accepts text, images, and video as input and outputs text, with support for six languages including English, Spanish, French, German, Italian, and Japanese.
  • It is released under the OpenMDW-1.1 license.

Summary of the nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 model card, 2026-10-16. The estimate below is for another build of the family.

What it is

Released byNVIDIA
Released2026-10-12
Parameters124.5B
VRAM75.0 GB for the weights

What it runs on

Memory and cards for NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 (NVFP4)

75.0 GBweights, file size
762 MBruntime overhead, at least

How much memory each request adds isn't estimated yet for this architecture. The weights need at least the cards below, plus room for the context.

CardWeights alone
RTX 3060 12 GB … L40S 48 GB
6 smaller cards
does not fit
A100 80 GB
FP4 without its speed-up here
tight
H100 80 GBdoes not fit
RTX PRO 6000 Blackwell 96 GBtight
DGX Spark (GB10) 128 GB unifiedfits
H200 141 GB
FP4 without its speed-up here
fits
B200 180 GBfits
8× H100 80 GB
tensor parallel
does not fit

Builds

Sizes, precisions & builds

BuildParamsPrecisionWeightsSmallest setup
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 ↗ 124.5BBF16 249 GBnone of the reference configurations
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 (above) ↗ 65.8BNVFP4 75.0 GBA100 80 GB
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B ↗
packaged by unsloth
124.5BBF16 249 GBnone of the reference configurations
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 ↗
packaged by unsloth
65.8BNVFP4 75.0 GBA100 80 GB
NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-GGUF ↗
packaged by unsloth
11 builds: IQ1_M 46.1 GB … Q8_0 132 GB
GGUF 65.8 GBH100 80 GB

From the model card

What NVIDIA says about NVIDIA-Nemotron-3.5-Super-VL

Read the model card

Description

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 is the full-precision release of NVIDIA Nemotron 3.5 Super VL, a vision-language model for reasoning, coding, tool use, document understanding, and video understanding. The BF16 weights support customization, domain adaptation, and full-precision evaluation. For optimized deployment, see the NVFP4 release.

The model combines interleaved Mamba-2, Mixture-of-Experts (MoE), and attention layers with a C-RADIO vision encoder and a vision-language connector. It has 120B total parameters and 12B active parameters and supports context lengths up to 1M tokens. Multi-Token Prediction (MTP) enables native speculative decoding.

This model is ready for commercial or non-commercial use.

Model Summary

Total Parameters120B (12B active)
ArchitectureMamba-2–Transformer hybrid Mixture of Experts with a C-RADIO vision encoder
PrecisionBF16 (full-precision reference weights)
Context LengthUp to 1M tokens; deployment settings are listed below
Inputs / OutputsText, images, and video / text
Deployment4× H100 or 2× GB200
Speculative DecodingNative Multi-Token Prediction (MTP)
Reasoning ModeConfigurable through the chat template (enable_thinking=True/False)
Recommended SamplingText: temperature 1.0, top_p 0.95; vision: temperature 0.6, top_p 0.95
Best ForCustomization, domain adaptation, and full-precision evaluation
LicenseOpenMDW-1.1

Model Overview

Model Developer: NVIDIA Corporation

What is Nemotron?

NVIDIA Nemotron™ is a family of open models with open weights, training data, and recipes, delivering leading efficiency and accuracy for building specialized AI agents.

Quick Start

For efficient inference on DGX Spark or data center GPUs, see the NVFP4 card. The example below serves BF16 on 4× H100.

Nemotron 3.5 Super VL has configurable reasoning levels: off, on (medium), and on. Selecting on (medium) will reduce the number of tokens generated compared to on, but may impact accuracy. You can learn about the reasoning budget here!

Use Docker with NVIDIA GPU support and the vllm/vllm-openai:nightly-3f1bcfd82bb2e93fcd364fc7a5491718453a77e2 or (vllm/vllm-openai:v0.32.0) image. If the model requires authentication, set HF_TOKEN in your shell before launching the container.

export MODEL=nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16
vllm serve $MODEL \
  --served-model-name nemotron-3.5-super-vl \
  --tensor-parallel-size 4 \
  --trust-remote-code \
  --gpu-memory-utilization=0.85 \
  --allowed-local-media-path=/ \
  --max-num-seqs=32 \
  --limit-mm-per-prompt '{"video": 1, "image": 1}' \
  --video-pruning-rate 0.5 \
  --media-io-kwargs '{"video": {"fps": 2, "num_frames": 256}}' \
  --enable-auto-tool-choice \
  --tool-call-parser qwen3_coder \
  --reasoning-parser nemotron_v3 \
  --speculative_config.method mtp \
  --speculative_config.num_speculative_tokens 3

Wait for the server to finish loading the model and compiling kernels, then check the endpoint:

curl --fail http://localhost:8000/v1/models

All vLLM and SGLang examples use port 8000 and the API model name nemotron-3.5-super-vl. See the API Client for text, image, video, and tool-calling examples.

License/Terms of Use

Governing Download Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1.

Benchmarks

We evaluated our model on the following benchmarks:

BenchmarkNemotron 3.5 SuperGA BF16Qwen3.5-122B-A10BQwen3.8-Flash-NextDeepSeek-V4-Flash0731Nemotron 3 UltraInkling-SmallGLM-5.3-Flash
GPQA-Diamond89.5286.792.789.687.088.691.2
HLE, text-only31.7924.239.635.829.233.336.47
MMLU-ProX76.9183.283.085.583.184.083.0
AA-Omniscience accuracy26.2026.227.945.025.335.434.2
AA-Omniscience hallucination rate30.084.541.395.922.159.630.3
AA-Omniscience index4.03-36.1-1.9-7.88.8-3.114.3
CritPt10.861.1—12.63.19.1—
APEX shortlist88.3654.791.888.775.071.577.1
LiveCodeBench v688.2276.794.194.689.288.380.6
SciCode, subtask47.1339.846.649.342.048.350.05
AA-LCR v1.168.6275.178.479.775.277.379.5
GraphWalks89.7186.176.165.463.355.788.4
IFBench70.8667.675.676.282.979.357.7
LMArena v3 proxy52.6747.035.249.834.353.661.0
WMT24++84.8086.386.285.582.784.686.1
tau2-bench retail69.2455.471.673.873.771.878.2
tau2-bench airline76.5063.580.883.579.862.781.8
tau2-bench telecom73.1987.290.498.283.090.198.5
tau3-bench banking18.8814.244.538.618.4—34.8
GDPval-AA v2121186115611382114813131563
BrowseComp7858.189.084.267.8574.186.6
SWE-bench Verified71.1071.978.775.473.975.2779.10
SWE-bench Multilingual67.7262.077.1072.068.871.478.30
SWE-bench Pro51.7844.960.3756.9149.6656.5459.70
Terminal-Bench 2.160.6748.7085.8076.8054.8053.4082.70
BabyVision29.6035.746.8N/AN/A24.739.1
MMLongBench-Doc62.8263.767.9N/A

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

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