Model reference · open weights
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.
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 by | NVIDIA |
|---|---|
| Released | 2026-10-12 |
| Parameters | 124.5B |
| VRAM | 75.0 GB for the weights |
What it runs on
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.
| Card | Weights 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 GB | does not fit |
| RTX PRO 6000 Blackwell 96 GB | tight |
| DGX Spark (GB10) 128 GB unified | fits |
| H200 141 GB FP4 without its speed-up here | fits |
| B200 180 GB | fits |
| 8× H100 80 GB tensor parallel | does not fit |
Builds
| Build | Params | Precision | Weights | Smallest setup |
|---|---|---|---|---|
| NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-BF16 ↗ | 124.5B | BF16 | 249 GB | none of the reference configurations |
| NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 ↗ | 65.8B | NVFP4 | 75.0 GB | A100 80 GB |
| NVIDIA-Nemotron-3.5-Super-VL-120B-A12B ↗ packaged by unsloth |
124.5B | BF16 | 249 GB | none of the reference configurations |
| NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4 (above) ↗ packaged by unsloth |
65.8B | NVFP4 | 75.0 GB | A100 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 GB | H100 80 GB |
From the model card
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.
| 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 Developer: NVIDIA Corporation
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.
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.
Governing Download Terms: Use of this model is governed by the OpenMDW License Agreement, version 1.1.
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 |
Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.