Nemotron 3.5 Super VL

Nemotron 3.5 Super VL

Open 74B vision-language model that runs locally on a 24GB GPU

NVIDIA's open-weight vision-language model (74B MoE, 9B active) that runs on a single 24GB GPU.

Model card on Hugging Face

Model card on Hugging Face

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Overview

What is Nemotron 3.5 Super VL?

Open 74B vision-language model that runs locally on a 24GB GPU

Nemotron 3.5 Super VL is NVIDIA's open-weight vision-language model: 74B parameters in a mixture-of-experts architecture with only 9B active per token, a 256K context window, and 2-bit quantization that lets it run locally on a single 24GB GPU.

It brings big-model vision-language understanding — images and text together, long documents, multi-image reasoning — to local RTX workstations and DGX Spark, without cloud APIs.

Released under the OpenMDW-1.1 license for research and commercial use.

Platforms and languages

Nemotron 3.5 Super VL Availability

Platforms

WindowsLinux

Languages

English

Capabilities

Nemotron 3.5 Super VL Key Features

74B MoE, 9B active

74 billion total parameters with only 9B active per token — big-model capability at a fraction of the compute.

Runs on a 24GB GPU

2-bit quantization squeezes the model onto a single 24GB card: local RTX workstations and DGX Spark.

Vision + language

A true vision-language model: understands images and text together.

256K context

Long-context window for documents, code and multi-image reasoning.

Open weights

Released under the OpenMDW-1.1 license for research and commercial use.

Best for

Who uses Nemotron 3.5 Super VL?

AI developers

Run a capable open vision-language model locally on consumer GPUs

Plans and access

Nemotron 3.5 Super VL Pricing

Free

Common questions

Nemotron 3.5 Super VL FAQs

What hardware do I need?

Thanks to 2-bit quantization, it runs on a single 24GB GPU such as an RTX card or NVIDIA DGX Spark.

Is it really open?

Yes — open weights under the OpenMDW-1.1 license.

How big is the model?

74B total parameters in a mixture-of-experts design, with 9B active per token, and a 256K context window.

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