NVIDIA Releases Nemotron 3.5 Tremendous VL, a 120B Open Multimodal Agent Mannequin

NVIDIA simply launched Nemotron 3.5 Super VL, an open vision-language mannequin constructed for always-on enterprise brokers. Nemotron 3.5 Tremendous VL is a 120B-parameter mixture-of-experts mannequin from NVIDIA that prompts 12B parameters per token. It reads textual content, photographs, paperwork and video, then causes, calls instruments and writes code in a single mannequin. For builders, 1 open checkpoint replaces a separate textual content mannequin together with a separate imaginative and prescient mannequin. NVIDIA additionally shipped 2 edge fashions, Nemotron 3.5 Bolt 74B-A9B and Bolt E4B.

TL;DR

Dimension: 120B whole parameters, 12B lively (MoE). Context as much as 1M tokens.
Runs on: 1x DGX Spark, 1x H200, 2x H100 or 1x GB200 with the NVFP4 checkpoint. A BF16 checkpoint can be printed.
Efficiency: Robust reasoning, agentic coding and doc imaginative and prescient. NVIDIA claims as much as 5x increased single-GPU throughput than open friends.
Finest: 89.90 on GPQA-Diamond (NVFP4).
Worst: 17.78 on tau3-bench banking (NVFP4).

What’s Nemotron 3.5 Tremendous VL?

It’s an open multimodal agent mannequin. Weights ship underneath the OpenMDW-1.1 license. NVIDIA additionally launched open datasets, coaching recipes and RL environments in NeMo Gymnasium.

The model card lists textual content, picture and video inputs with textual content output. Video is restricted to MP4 clips as much as 2 minutes. Reasoning may be toggled on or off via the chat template.

How does the structure work?

The spine interleaves Mamba-2, mixture-of-experts and a spotlight layers. Every MoE layer holds 512 routed consultants. A C-RADIO imaginative and prescient encoder and a connector feed photographs, doc pages and video frames into the language spine.

Velocity comes from 4 strategies:

Multi-token prediction: a local drafter proposes a number of tokens, verified in 1 ahead go.
LiLiCorr: a speculative drafter for DGX Spark, as much as 2.3x quicker token technology.
NVFP4 with quantization-aware distillation: 4-bit weights and activations on Blackwell, 4-bit weights with 16-bit activations elsewhere.
Context reuse: prefix caching retains each consideration caches and Mamba states throughout agent turns.

The mannequin was distilled from Nemotron 3 Extremely. NVIDIA group states that it educated on about 26 trillion tokens. Publish-training used roughly 3.8 million samples plus RL throughout dozens of environments. Continued pretraining added 10B Indic tokens.

How does it carry out on benchmarks?

All scores under are NVIDIA-reported, from the NVFP4 checkpoint on the mannequin card.

Space	Benchmark	Rating
Reasoning	GPQA-Diamond	89.90
Coding	SWE-bench Verified	71.07
Coding	Terminal-Bench 2.1	60.80
Internet agent	BrowseComp	77.5
Instruction following	IFBench	71.50
Charts	CharXiv reasoning	79.88
Paperwork	MMLongBench-Doc	62.82
Pc use	OSWorld-Verified	52.3
Lengthy-horizon agent	tau3-bench banking	17.78

On agent harness evals, Tremendous VL beats Nemotron 3 Extremely. It scores 63.44% on Hermes Bench versus 56.33%. On LangChain evals it scores 87.05% versus 84.3%. Extremely carries about 4.6x extra whole parameters.

NVIDIA group experiences over 600 output tokens per second on Synthetic Evaluation agentic duties by way of a Baseten endpoint. Its chart marks a 5x velocity hole over Inkling Small.

<div class="panel on" id="p-ov">
<div class="grid">
<div class="stat"><b data-n="120">0</b><span>B whole parameters</span></div>
<div class="stat"><b data-n="12">0</b><span>B lively per token</span></div>
<div class="stat"><b data-n="1" data-s="M">0</b><span>token context</span></div>
<div class="stat"><b data-n="512">0</b><span>routed consultants per MoE layer</span></div>
</div>
<div class="moe">
<div class="lbl"><span>MoE routing: every token lights up a small slice of the community</span><span id="mtp-tok">token #1</span></div>
<div class="cells" id="mtp-cells"></div>
</div>
<p class="word"><robust>What it’s:</robust> an open vision-language mannequin for always-on brokers. It reads textual content, photographs, paperwork and video, then causes, calls instruments and writes code. Weights ship underneath OpenMDW-1.1 with open datasets and recipes.</p>
</div>

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<div class="pipe" id="mtp-pipe">
<div class="stage on" data-i="0">Inputs<small>textual content, picture, video</small></div><div class="arr"></div>
<div class="stage" data-i="1">C-RADIO<small>imaginative and prescient encoder</small></div><div class="arr"></div>
<div class="stage" data-i="2">Hybrid MoE<small>Mamba-2 + consideration</small></div><div class="arr"></div>
<div class="stage" data-i="3">MTP decode<small>+ LiLiCorr</small></div><div class="arr"></div>
<div class="stage" data-i="4">Output<small>textual content, software calls</small></div>
</div>
<div class="element" id="mtp-detail"></div>
<div class="ctrl">
<button class="btn" id="mtp-prev"> Again</button>
<button class="btn pri" id="mtp-next">Subsequent </button>
<button class="btn" id="mtp-play"> Auto-play</button>
<span class="step" id="mtp-step">Step 1 of 5</span>
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<div class="panel" id="p-bm">
<div class="seg"><button class="on" data-c="nv">NVFP4</button><button data-c="bf">BF16</button></div>
<div id="mtp-bars"></div>
<p class="word">NVIDIA-reported scores from the mannequin card. Orange bar marks the weakest outcome.</p>
</div>

<div class="panel" id="p-cp">
<div class="cmp" id="mtp-cmp"></div>
<p class="legend">Vendor self-reported scores. Tremendous VL makes use of NVFP4. Harnesses differ: Inkling-Small SWE-bench makes use of a bash-only harness.</p>
</div>

<div class="panel" id="p-dp">
<div class="hw" id="mtp-hw">
<button class="chip on" data-h="0">1x DGX Spark<small>vLLM · LiLiCorr</small></button>
<button class="chip" data-h="1">1x H200<small>vLLM · MTP 3</small></button>
<button class="chip" data-h="2">2x H100<small>vLLM · MTP 3</small></button>
<button class="chip" data-h="3">1x GB200<small>vLLM · MTP 5</small></button>
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<div class="element" id="mtp-hwd"></div>
<pre id="mtp-code"></pre>
<p class="word">Instructions abridged from the Hugging Face mannequin card. Use the complete flag set there for manufacturing.</p>
</div>

<div class="ft"><span>Sources: NVIDIA weblog, NVIDIA technical weblog, Hugging Face mannequin playing cards</span><span class="br">© Marktechpost</span></div>
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How does it evaluate with comparable open fashions?

Function	Nemotron 3.5 Tremendous VL	Qwen3.5-122B-A10B	Inkling-Small	Nemotron 3 Ultra
Developer	NVIDIA	Alibaba Qwen	Pondering Machines Lab	NVIDIA
Complete / lively params	120B / 12B	122B / 10B	276B / 12B	550B / 55B
Context	As much as 1M	262,144 native, extensible to 1,010,000	Not disclosed on mannequin card	Evaluated at 1M (Ruler @1M)
Inputs	Textual content, picture, video	Textual content, picture, video	Textual content, picture, audio	Not disclosed
License	OpenMDW-1.1	Apache 2.0	Apache 2.0	OpenMDW-1.1
Reference {hardware}	1x DGX Spark, 1x H200, 2x H100 or 1x GB200	8-GPU tensor-parallel serving instance	Not disclosed	Not disclosed
GPQA Diamond	89.90	86.6	89.5	Not disclosed
SWE-bench Verified	71.07	72.0	80.2 (bash-only harness)	65 to 70.4 (throughout 5 harnesses)
IFBench	71.50	76.1	82.2	82
CharXiv reasoning	79.88	77.2	77.4	Not disclosed
BrowseComp	77.5	63.8	77.4 (with context administration)	Not disclosed
Terminal-Bench	60.80 (v2.1)	49.4 (v2)	64.7 (v2.1, finest harness)	54 (v2.0)

Scores are self-reported by every vendor. Harnesses and benchmark variations differ, so deal with gaps of some factors with warning.

How can builders deploy it?

Tremendous VL is obtainable on Hugging Face, OpenRouter, ModelScope and as an NVIDIA NIM. Supported runtimes are vLLM, SGLang and TensorRT-LLM on Blackwell and Hopper. Native paths embody llama.cpp and Unsloth.

For manufacturing, Dynamo recipes add KV-aware routing. On 16 B200 GPUs at 50 tokens per second per consumer, NVIDIA experiences 8x extra customers served. First-token latency drops 85%.

Customization runs via NeMo AutoModel for SFT and LoRA, and NeMo RL for reinforcement studying. NVIDIA cites early outcomes:

Scale AI for Skydance vendor credit score processing: 40x decrease inference value and 5x decrease latency.
Ramp receipt-to-JSON extraction: as much as 50% increased throughput and 10x decrease latency anticipated.
AI21 for Alice risk intelligence: 40% increased accuracy.

What are the Nemotron 3.5 Bolt fashions?

Nemotron 3.5 Bolt 74B-A9B targets native brokers. NVIDIA’s launch notes describe it as distilled from Tremendous VL. Native 2-bit quantization matches it into 24GB of reminiscence on RTX and DGX Spark methods. NVIDIA says it ranks #1 amongst open fashions in its class on PinchBench. It ranks #2 on BrowseComp amongst open fashions. It’s obtainable now on Hugging Face with llama.cpp, Ollama, LM Studio, Unsloth and vLLM help.

Bolt E4B 4GB arrives October 23. It targets voice assistants and embedded brokers on GeForce RTX, Jetson and RTX or DGX Spark.

Key Takeaways

120B MoE, 12B lively, 1M context, open weights underneath OpenMDW-1.1.
1 mannequin handles textual content, photographs, paperwork, video, instruments and code.
NVFP4 checkpoint runs on a single DGX Spark, H200 or GB200.
89.90 GPQA-Diamond, however solely 17.78 on tau3-bench banking.
Bolt 74B-A9B brings a 24GB native agent mannequin to RTX PCs.

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The put up NVIDIA Releases Nemotron 3.5 Tremendous VL, a 120B Open Multimodal Agent Mannequin appeared first on MarkTechPost.

NVIDIA Releases Nemotron 3.5 Tremendous VL, a 120B Open Multimodal Agent Mannequin

TL;DR

What’s Nemotron 3.5 Tremendous VL?

How does the structure work?

How does it carry out on benchmarks?

How does it evaluate with comparable open fashions?

How can builders deploy it?

What are the Nemotron 3.5 Bolt fashions?

Key Takeaways

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