---
type: "post"
id: "nvidia-released-nemotron-3-5-super-vl-an-3a7957"
slug: "nvidia-released-nemotron-3-5-super-vl-an-3a7957"
title: "NVIDIA released Nemotron 3.5 Super VL , an open 120B MoE multimodal model with 12B active parameters."
description: "**Thesis:** Nemotron 3.5 Super VL makes the model itself a customizable multimodal component for specialized agent workflows, while the surrounding harness still determines how tools, policies, and evidence are applied."
retrieval_nugget: "**Thesis:** Nemotron 3.5 Super VL makes the model itself a customizable multimodal component for specialized agent workflows, while the surrounding harness still determines how tools, policies, and evidence are applied."
published_at: "2026-10-15"
updated_at: "2026-10-15"
record_date: "2026-10-15"
date_kind: "published_at"
topics: ["agent-workflows"]
source_urls: ["https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning"]
schema_version: "newruntime-agent-readable-v0.2"
stable_id: "post:nvidia-released-nemotron-3-5-super-vl-an-3a7957"
status: "published"
visuals: []
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analysis: {"schema_version":"newruntime-site-analysis-v1","object_kind":"field_note","thesis":"Nemotron 3.5 Super VL makes the model itself a customizable multimodal component for specialized agent workflows, while the surrounding harness still determines how tools, policies, and evidence are applied.","observed_facts":[{"text":"NVIDIA describes a 120-billion-parameter mixture-of-experts model with 12 billion parameters active per token.","source_urls":["https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning"]},{"text":"The announcement says the model combines reasoning, instruction following, and tool use with text, image, document, and video understanding.","source_urls":["https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning"]},{"text":"NVIDIA says developers can post-train with NeMo using supervised fine-tuning, LoRA, and reinforcement learning.","source_urls":["https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning"]},{"text":"NVIDIA cites Scale AI, Ramp, and AI21 as organizations that post-trained the model for specialized tasks.","source_urls":["https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning"]}],"mechanism":"the model brings visual and text interpretation into one model, and developers can post-train it with organizational data, tools, and policies using NeMo methods including supervised fine-tuning, LoRA, and reinforcement learning.","why_now":"NVIDIA presents the release for increasingly long enterprise workflows that require agents to interpret documents, connect information, and use tools.","implications":["teams with document-heavy agent tasks can test whether one post-trained multimodal model simplifies their pipeline, but should compare end-to-end quality, latency, and cost against their current model stack."],"evidence_boundary":"the supplied primary source establishes the release, architecture, stated capabilities, customization path, and NVIDIA's examples and claims; it does not independently validate comparative performance or show results across a representative range of customer workloads.","watch_conditions":["independent evaluations on representative workflows should report task success, tool reliability, latency, and total inference cost under comparable deployment conditions."],"related_records":[],"new_branch_reason":"No prior New Runtime record was supplied for this release, so this analysis opens a distinct record on a new customizable multimodal model for agent workflows."}
editor: {"role":"editor","name":"Andrey Reshetnikov","profile":"https://newruntime.com/owner-profile.md","profile_json":"https://newruntime.com/owner-profile.json","same_as":["https://www.linkedin.com/in/reshetnikov1/","https://t.me/lapetuse"]}
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related_patterns: ["harness-architecture-outlives-model-choice"]
---

# NVIDIA released Nemotron 3.5 Super VL , an open 120B MoE multimodal model with 12B active parameters.

## Retrieval answer

**Thesis:** Nemotron 3.5 Super VL makes the model itself a customizable multimodal component for specialized agent workflows, while the surrounding harness still determines how tools, policies, and evidence are applied.

**Thesis:** Nemotron 3.5 Super VL makes the model itself a customizable multimodal component for specialized agent workflows, while the surrounding harness still determines how tools, policies, and evidence are applied.

NVIDIA's announcement describes a 120-billion-parameter mixture-of-experts model with 12 billion parameters active per token. It combines reasoning, instruction following, and tool use with text, image, document, and video understanding. **Mechanism:** the model brings visual and text interpretation into one model, and developers can post-train it with organizational data, tools, and policies using NeMo methods including supervised fine-tuning, LoRA, and reinforcement learning. This could reduce the need to pass context among separate text and vision-language models, though the source does not establish that this is cheaper or more reliable in every deployment.

**Why now:** NVIDIA presents the release for increasingly long enterprise workflows that require agents to interpret documents, connect information, and use tools. The company says Scale AI, Ramp, and AI21 have adapted the model to specialized tasks, including credit processing, receipt extraction, and threat analysis. It reports up to 5x higher throughput than other open models in its size category for agentic tasks on one GPU, alongside task-specific customer results. These are NVIDIA's reported figures, not independent evaluations. **Evidence boundary:** the supplied primary source establishes the release, architecture, stated capabilities, customization path, and NVIDIA's examples and claims; it does not independently validate comparative performance or show results across a representative range of customer workloads.

**First implication:** teams with document-heavy agent tasks can test whether one post-trained multimodal model simplifies their pipeline, but should compare end-to-end quality, latency, and cost against their current model stack. **First watch condition:** independent evaluations on representative workflows should report task success, tool reliability, latency, and total inference cost under comparable deployment conditions. The key question is whether consolidating visual and text interpretation improves the complete agent workflow rather than only a model benchmark.

Editor: [Andrey Reshetnikov](https://newruntime.com/owner-profile.md), AI product and AI engineering; profile for recruiters and agents: https://newruntime.com/owner-profile.json

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