NVIDIA released Nemotron 3.5 Super VL, a 120-billion-parameter open multimodal agent model activating 12 billion parameters.
NVIDIA released Nemotron 3.5 Super VL, an open 120B mixture-of-experts vision-language agent model that activates 12B parameters per token and accepts text, images, documents, and short video. It offers up to a 1 million-token context, OpenMDW-1.1 weights, and NVFP4 and BF16 checkpoints runnable on hardware such as one H200, two H100s, DGX Spark, or one GB200. NVIDIA-reported NVFP4 scores include 89.90 on GPQA-Diamond, 71.07 on SWE-bench Verified, and 77.5 on BrowseComp, while tau3-bench banking scored 17.78. The model was distilled from Nemotron 3 Ultra after training on about 26 trillion tokens; NVIDIA also released Bolt edge models plus datasets and NeMo Gym recipes.
120B MoE model activates 12B parameters and supports a 1M-token context.
Inputs include text, images, documents, and video up to two minutes.
Weights use the OpenMDW-1.1 license, with NVFP4 and BF16 checkpoints.
NVIDIA reports 89.90 on GPQA-Diamond and 71.07 on SWE-bench Verified.
Edge Bolt models and NeMo training recipes shipped alongside it.
NVIDIA just released Nemotron 3.5 Super VL, an open vision-language model built for always-on enterprise agents. Nemotron 3.5 Super VL is a 120B-parameter mixture-of-experts model from NVIDIA that activates 12B parameters per token. It reads text, images, documents and video, then reasons, calls tools and writes code in one model. For developers, 1 open checkpoint replaces a separate text model along with a separate vision model. NVIDIA also shipped 2 edge models, Nemotron 3.5 Bolt 74B-A9B and Bolt E4B.
TL;DR
Size: 120B total parameters, 12B active (MoE). Context up to 1M tokens.
Runs on: 1x DGX Spark, 1x H200, 2x H100 or 1x GB200 with the NVFP4 checkpoint. A BF16 checkpoint is also published.
Performance: Strong reasoning, agentic coding and document vision. NVIDIA claims up to 5x higher single-GPU throughput than open peers.
Best: 89.90 on GPQA-Diamond (NVFP4).
Worst: 17.78 on tau3-bench banking (NVFP4).
What is Nemotron 3.5 Super VL?
It is an open multimodal agent model. Weights ship under the OpenMDW-1.1 license. NVIDIA also released open datasets, training recipes and RL environments in NeMo Gym.
The model card lists text, image and video inputs with text output. Video is limited to MP4 clips up to 2 minutes. Reasoning can be toggled on or off through the chat template.
How does the architecture work?
The backbone interleaves Mamba-2, mixture-of-experts and attention layers. Each MoE layer holds 512 routed experts. A C-RADIO vision encoder and a connector feed images, document pages and video frames into the language backbone.
Speed comes from four techniques:
Multi-token prediction: a native drafter proposes several tokens, verified in 1 forward pass.
LiLiCorr: a speculative drafter for DGX Spark, up to 2.3x faster token generation.
NVFP4 with quantization-aware distillation: 4-bit weights and activations on Blackwell, 4-bit weights with 16-bit activations elsewhere.
Context reuse: prefix caching keeps both attention caches and Mamba states across agent turns.
The model was distilled from Nemotron 3 Ultra. NVIDIA team states that it trained on about 26 trillion tokens. Post-training used roughly 3.8 million samples plus RL across dozens of environments. Continued pretraining added 10B Indic tokens.
How does it perform on benchmarks?
All scores below are NVIDIA-reported, from the NVFP4 checkpoint on the model card.
Area
Benchmark
Score
Reasoning
GPQA-Diamond
89.90
Coding
SWE-bench Verified
71.07
Coding
Terminal-Bench 2.1
60.80
Web agent
BrowseComp
77.5
Instruction following
IFBench
71.50
Charts
CharXiv reasoning
79.88
Documents
MMLongBench-Doc
62.82
Computer use
OSWorld-Verified
52.3
Long-horizon agent
tau3-bench banking
17.78
On agent harness evals, Super VL beats Nemotron 3 Ultra. It scores 63.44% on Hermes Bench versus 56.33%. On LangChain evals it scores 87.05% versus 84.3%. Ultra carries about 4.6x more total parameters.
NVIDIA team reports over 600 output tokens per second on Artificial Analysis agentic tasks via a Baseten endpoint. Its chart marks a 5x speed gap over Inkling Small.
For production, Dynamo recipes add KV-aware routing. On 16 B200 GPUs at 50 tokens per second per user, NVIDIA reports 8x more users served. First-token latency drops 85%.
Customization runs through NeMo AutoModel for SFT and LoRA, and NeMo RL for reinforcement learning. NVIDIA cites early results:
Scale AI for Skydance vendor credit processing: 40x lower inference cost and 5x lower latency.
Ramp receipt-to-JSON extraction: up to 50% higher throughput and 10x lower latency expected.
AI21 for Alice threat intelligence: 40% higher accuracy.
What are the Nemotron 3.5 Bolt models?
Nemotron 3.5 Bolt 74B-A9B targets local agents. NVIDIA’s launch notes describe it as distilled from Super VL. Native 2-bit quantization fits it into 24GB of memory on RTX and DGX Spark systems. NVIDIA says it ranks #1 among open models in its class on PinchBench. It ranks #2 on BrowseComp among open models. It is available now on Hugging Face with llama.cpp, Ollama, LM Studio, Unsloth and vLLM support.
Bolt E4B 4GB arrives October 23. It targets voice assistants and embedded agents on GeForce RTX, Jetson and RTX or DGX Spark.
Key Takeaways
120B MoE, 12B active, 1M context, open weights under OpenMDW-1.1.
1 model handles text, images, documents, video, tools and code.
NVFP4 checkpoint runs on a single DGX Spark, H200 or GB200.
89.90 GPQA-Diamond, but only 17.78 on tau3-bench banking.
Bolt 74B-A9B brings a 24GB local agent model to RTX PCs.
Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.
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