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

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.

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](#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:

| Benchmark | Nemotron 3.5 SuperGA BF16 | Qwen3.5-122B-A10B | Qwen3.8-Flash-Next | DeepSeek-V4-Flash0731 | Nemotron 3 Ultra | Inkling-Small | GLM-5.3-Flash | | --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | | GPQA-Diamond | 89.52 | 86.7 | 92.7 | 89.6 | 87.0 | 88.6 | 91.2 | | HLE, text-only | 31.79 | 24.2 | 39.6 | 35.8 | 29.2 | 33.3 | 36.47 | | MMLU-ProX | 76.91 | 83.2 | 83.0 | 85.5 | 83.1 | 84.0 | 83.0 | | AA-Omniscience accuracy | 26.20 | 26.2 | 27.9 | 45.0 | 25.3 | 35.4 | 34.2 | | AA-Omniscience hallucination rate | 30.0 | 84.5 | 41.3 | 95.9 | 22.1 | 59.6 | 30.3 | | AA-Omniscience index | 4.03 | -36.1 | -1.9 | -7.8 | 8.8 | -3.1 | 14.3 | | CritPt | 10.86 | 1.1 | — | 12.6 | 3.1 | 9.1 | — | | APEX shortlist | 88.36 | 54.7 | 91.8 | 88.7 | 75.0 | 71.5 | 77.1 | | LiveCodeBench v6 | 88.22 | 76.7 | 94.1 | 94.6 | 89.2 | 88.3 | 80.6 | | SciCode, subtask | 47.13 | 39.8 | 46.6 | 49.3 | 42.0 | 48.3 | 50.05 | | AA-LCR v1.1 | 68.62 | 75.1 | 78.4 | 79.7 | 75.2 | 77.3 | 79.5 | | GraphWalks | 89.71 | 86.1 | 76.1 | 65.4 | 63.3 | 55.7 | 88.4 | | IFBench | 70.86 | 67.6 | 75.6 | 76.2 | 82.9 | 79.3 | 57.7 | | LMArena v3 proxy | 52.67 | 47.0 | 35.2 | 49.8 | 34.3 | 53.6 | 61.0 | | WMT24++ | 84.80 | 86.3 | 86.2 | 85.5 | 82.7 | 84.6 | 86.1 | | tau2-bench retail | 69.24 | 55.4 | 71.6 | 73.8 | 73.7 | 71.8 | 78.2 | | tau2-bench airline | 76.50 | 63.5 | 80.8 | 83.5 | 79.8 | 62.7 | 81.8 | | tau2-bench telecom | 73.19 | 87.2 | 90.4 | 98.2 | 83.0 | 90.1 | 98.5 | | tau3-bench banking | 18.88 | 14.2 | 44.5 | 38.6 | 18.4 | — | 34.8 | | GDPval-AA v2 | 1211 | 861 | 1561 | 1382 | 1148 | 1313 | 1563 | | BrowseComp | 78 | 58.1 | 89.0 | 84.2 | 67.85 | 74.1 | 86.6 | | SWE-bench Verified | 71.10 | 71.9 | 78.7 | 75.4 | 73.9 | 75.27 | 79.10 | | SWE-bench Multilingual | 67.72 | 62.0 | 77.10 | 72.0 | 68.8 | 71.4 | 78.30 | | SWE-bench Pro | 51.78 | 44.9 | 60.37 | 56.91 | 49.66 | 56.54 | 59.70 | | Terminal-Bench 2.1 | 60.67 | 48.70 | 85.80 | 76.80 | 54.80 | 53.40 | 82.70 | | BabyVision | 29.60 | 35.7 | 46.8 | N/A | N/A | 24.7 | 39.1 | | MMLongBench-Doc | 62.82 | 63.7 | 67.9 | N/A | N/A | 49.06 | 65.8 | | V* | 85.60 | 91.1 | 93.0 | N/A | N/A | 70.5 | 88.1 | | OCR-Reasoning | 65.08 | 64.3 | 67.1 | N/A | N/A | 54.7 | 64.6 | | MathVision | 78.16 | 83.3 | 87.5 | N/A | N/A | 78.8 | 82.5 | | HLE, full set | 29.20 | 24.4 | 38.8 | N/A | N/A | 31.6 | 35.1 | | OCRBench v2, EN | 68.12 | 68.3 | 67.9 | N/A | N/A | 54.2 | 61.8 | | OCRBench v2, ZH | 59.15 | 67.3 | 68.3 | N/A | N/A | 52.4 | 56.8 | | CharXiv reasoning | 80.82 | 83.5 |...