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Image-Text-to-Text

Transformers

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PyTorch

nvidia/nemotron-post-training-v3

nvidia/nemotron-pre-training-datasets

6 languages

nemotron_h_omni

nvidia

nemotron-3.5

conversational

custom_code

License: openmdw-1.1

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Use this model Instructions to use nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

Transformers How to use nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 with Transformers:

Use a pipeline as a high-level helper

from transformers import pipeline

pipe = pipeline("image-text-to-text", model="nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", trust_remote_code=True) messages = [ { "role": "user", "content": [ {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"}, {"type": "text", "text": "What animal is on the candy?"} ] }, ] pipe(text=messages)

pip install -U transformers accelerate

Load model directly

from transformers import AutoModelForMultimodalLM model = AutoModelForMultimodalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", trust_remote_code=True, device_map="auto")

Notebooks

Google Colab

Kaggle

Local Apps Settings

vLLM How to use nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 with vLLM:

Install vLLM from pip:

pip install vllm

Start the vLLM server:

vllm serve "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16"

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:8000/v1/chat/completions"
-H "Content-Type: application/json"
--data '{ "model": "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

Use Docker

docker model run hf.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16

SGLang How to use nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 with SGLang:

Install SGLang from pip:

pip install sglang

Start the SGLang server:

python3 -m sglang.launch_server
--model-path "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16"
--host 0.0.0.0
--port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions"
-H "Content-Type: application/json"
--data '{ "model": "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

Use Docker images

docker run --gpus all
--shm-size 32g
-p 30000:30000
-v ~/.cache/huggingface:/root/.cache/huggingface
--env "HF_TOKEN=<secret>"
--ipc=host
lmsysorg/sglang:latest
python3 -m sglang.launch_server
--model-path "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16"
--host 0.0.0.0
--port 30000

Call the server using curl (OpenAI-compatible API):

curl -X POST "http://localhost:30000/v1/chat/completions"
-H "Content-Type: application/json"
--data '{ "model": "nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16", "messages": [ { "role": "user", "content": [ { "type": "text", "text": "Describe this image in one sentence." }, { "type": "image_url", "image_url": { "url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg" } } ] } ] }'

Docker Model Runner How to use nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 with Docker Model Runner:

docker model run hf.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Crawled and Scraped from Online Sources by NVIDIA</strong>","children":[],"isValid":true,"title":"Crawled and Scraped from Online Sources by NVIDIA"}],"isValid":true,"title":"Training, Testing, and Evaluation Datasets"},{"id":"private-non-publicly-accessible-datasets-of-third-parties","label":"Private Non-publicly Accessible Datasets of Third Parties","children":[],"isValid":true,"title":"Private Non-publicly Accessible Datasets of Third Parties"},{"id":"private-non-publicly-accessible-datasets-by-nvidia","label":"Private Non-publicly Accessible Datasets by NVIDIA","children":[],"isValid":true,"title":"Private Non-publicly Accessible Datasets by NVIDIA"},{"id":"nvidia-sourced-synthetic-datasets-pre-training","label":"NVIDIA-Sourced Synthetic Datasets (Pre-Training)","children":[],"isValid":true,"title":"NVIDIA-Sourced Synthetic Datasets (Pre-Training)"},{"id":"nvidia-sourced-synthetic-datasets-post-training","label":"NVIDIA-Sourced Synthetic Datasets (Post-Training)","children":[],"isValid":true,"title":"NVIDIA-Sourced Synthetic Datasets (Post-Training)"},{"id":"language-distribution-in-post-training","label":"Language Distribution in Post-Training","children":[{"id":"additional-training-data","label":"Additional Training Data","children":[],"isValid":true,"title":"Additional Training Data"}],"isValid":true,"title":"Language Distribution in Post-Training"},{"id":"testing-datasets","label":"Testing Datasets","children":[],"isValid":true,"title":"Testing Datasets"},{"id":"evaluation-datasets","label":"Evaluation Datasets","children":[],"isValid":true,"title":"Evaluation Datasets"},{"id":"inference","label":"Inference","children":[],"isValid":true,"title":"Inference"},{"id":"ethical-considerations","label":"Ethical Considerations","children":[],"isValid":true,"title":"Ethical Considerations"}],"isValid":true,"title":"NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16"}]}"> NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 Description

Model Summary

Model Overview What is Nemotron?

Quick Start

License/Terms of Use

Benchmarks Agentic Coding Benchmarks

Deployment Geography: Global

Use Case

Release Date

Reference(s)

Model Architecture

Model Design

Training Methodology

Input

Output

Software Integration

Model Version(s)

Quick Start Guide Choose your deployment

Speculative Decoding

vLLM

SGLang

API Client

Training, Testing, and Evaluation Datasets Training

Public Datasets

Crawled and Scraped from Online Sources by NVIDIA

Private Non-publicly Accessible Datasets of Third Parties

Private Non-publicly Accessible Datasets by NVIDIA

NVIDIA-Sourced Synthetic Datasets (Pre-Training)

NVIDIA-Sourced Synthetic Datasets (Post-Training)

Language Distribution in Post-Training Additional Training Data

Testing Datasets

Evaluation Datasets

Inference

Ethical Considerations

NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16

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 Parameters 120B (12B active)

Architecture Mamba-2–Transformer hybrid Mixture of Experts with a C-RADIO vision encoder

Precision BF16 (full-precision reference weights)

Context Length Up to 1M tokens; deployment settings are listed below

Inputs / Outputs Text, images, and video / text

Deployment 4× H100 or 2× GB200

Speculative Decoding Native Multi-Token Prediction (MTP)

Reasoning Mode Configurable through the chat template (enable_thinking=True/False)

Recommended Sampling Text: temperature 1.0, top_p 0.95; vision: temperature 0.6, top_p 0.95

Best For Customization, domain adaptation, and full-precision evaluation

License OpenMDW-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.

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:

We evaluated Nemotron 3.5 Super on a comprehensive suite of benchmarks spanning agentic capabilities, reasoning and knowledge, multimodal, conversational ability and instruction following, long-context understanding, and multilingual performance. All evaluation results for Nemotron 3.5 Super and baselines were collected via NeMo Gym. For reproducibility purposes, more details on the evaluation settings can be found in the NeMo Gym recipes folder and the corresponding reproducibility tutorial. The published recipes specify the benchmark-specific containers, prompts, inference parameters, parser configurations, and scoring settings used to produce the results.

There were a few benchmarks that were not yet onboarded in our open source tools, and for these we used either their official open source implementation or an internal scaffolding that we plan to open source in the future: BabyVision, CharXiv-RQ, MathVision, MMLongBench-Doc, OCR-Reasoning, OCRBench-v2, V*, Video-MME-v2.

Agentic Coding Benchmarks

Additional harness-level coding-agent results for SWE-Bench Pro and Terminal-Bench 2.1 are shown below.

Deployment Geography: Global

Use Case

This model is intended for developers who want to customize a post-trained vision-language model, adapt it to domain-specific data, develop quantized variants, or run full-precision inference and evaluation.

Release Date

Hugging Face - 10/15/2026 via Hugging Face

Reference(s)

NVIDIA Nemotron 3.5 Super VL and Lightning Technical Report

Model Architecture

Architecture Type: Mamba-2–Transformer hybrid Mixture of Experts (MoE) with Multi-Token Prediction (MTP)

Network Architecture: Nemotron Hybr