---
type: "post"
id: "but-meta-isn-t-the-only-company-seeking-5bc9dc"
slug: "but-meta-isn-t-the-only-company-seeking-5bc9dc"
title: "But Meta isn't the only company seeking to claim its piece of the US open source ecosystem."
description: "Thesis: Nemotron 3.5 Super VL packages multimodal perception, reasoning, and tool use in an open model that organizations can adapt to specialized agent workflows."
retrieval_nugget: "Thesis: Nemotron 3.5 Super VL packages multimodal perception, reasoning, and tool use in an open model that organizations can adapt to specialized agent workflows."
published_at: "2026-10-16"
updated_at: "2026-10-16"
record_date: "2026-10-16"
date_kind: "published_at"
topics: ["tool-use","agent-workflows"]
source_urls: ["https://developer.nvidia.com/blog/nvidia-nemotron-3-5-super-vl-powers-specialized-agents-with-multimodal-reasoning?ncid=so-twit-170075"]
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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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---

# But Meta isn't the only company seeking to claim its piece of the US open source ecosystem.

## Retrieval answer

Thesis: Nemotron 3.5 Super VL packages multimodal perception, reasoning, and tool use in an open model that organizations can adapt to specialized agent workflows.

Thesis: Nemotron 3.5 Super VL packages multimodal perception, reasoning, and tool use in an open model that organizations can adapt to specialized agent workflows.

Observed facts: NVIDIA describes a 120-billion-parameter mixture-of-experts model with 12 billion parameters active per token. Its announcement says the model handles text, images, documents, and video alongside reasoning, instruction following, and tool use. NVIDIA says teams can post-train it with NeMo using proprietary data, tools, policies, and workflows, and that it is releasing supporting datasets on Hugging Face.

Mechanism: A single model that can interpret visual and textual inputs while calling tools may reduce the need to route a workflow through separate vision-language and text models. Post-training provides a path to encode domain behavior, while the organization's own tools and policies define how the model is applied. This is a proposed operating advantage, not proof that every deployment can eliminate component models.

Why now: The release is framed for longer enterprise workflows that combine documents, policy checks, evidence gathering, and tool calls; making a customizable multimodal foundation available gives builders a new option for those workflows.

Implications: Teams can evaluate whether one adaptable model simplifies a multimodal agent stack compared with separate model stages. NVIDIA cites customer post-training examples, but those reported gains are not independently validated in the supplied source.

Evidence boundary: The supplied public source is NVIDIA's announcement and technical blog. Its throughput comparison, benchmark figures, and customer outcome numbers are vendor-reported; the source does not provide independent validation in the material available here.

Watch: Whether independent evaluations reproduce the claimed throughput and task-quality results on representative enterprise workflows. Also watch for deployment details on supported hardware, reproducible evaluations, and evidence that customization improves task success without introducing unacceptable operating costs.

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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