NVIDIA Nemotron 3.5 Super VL: Open 120B Model Guide

What Nemotron 3.5 Super VL Can Do

Super VL is intended to process visual material alongside text. NVIDIA describes support for tasks involving PDFs, charts, tables, images, and webpages, as well as instruction following and tool use. These capabilities could support workflows such as extracting figures from a report, answering questions about a chart, or using visual context to complete a multi-step task.

The model is part of NVIDIA’s Nemotron family, but its focus is visual-language understanding. It should not be treated as interchangeable with other Nemotron models that may target different tasks or configurations. The “120 billion” figure describes the model’s parameter count; it does not, by itself, specify how much memory a particular deployment requires.

How to Interpret NVIDIA’s Benchmark Claims

NVIDIA reports that Nemotron 3.5 Super VL beat Claude Opus 5 and GPT-6.1 Sol on specialized task benchmarks. The distinction matters: benchmark outcomes depend on the tasks, datasets, evaluation methods, and model configurations being compared. A result on a particular test does not establish a general advantage across coding, everyday assistance, or every form of visual reasoning.

For teams considering the model, the reported scores are a reason to examine the underlying evaluations, not a substitute for testing. Run representative documents and images from your own workflow, and check accuracy, latency, reliability, and operating cost. This is especially important for tasks where a missed table value or misread chart could affect a decision.

Training, GPU Support, and Availability

NVIDIA reports that Super VL was trained with approximately 26 trillion pretraining and continued-pretraining tokens. Its post-training included 3.8 million samples, followed by reinforcement learning across roughly 100 open environments. These figures describe the scale of the reported training process; they do not guarantee performance on any specific application.

The model is optimized for NVIDIA Blackwell and Hopper systems, with reported support for Ampere-generation GPUs as well. NVIDIA also names GeForce RTX PCs, DGX Station, and RTX PRO workstations as systems for the model. Actual feasibility depends on the GPU configuration and deployment choices, including precision, context length, and serving setup. Check the model’s current documentation for concrete requirements before planning a deployment.

NVIDIA says Nemotron 3.5 Super VL is available on Hugging Face. Explore its model information and hardware requirements before testing it on your own workloads, and compare results with the systems you already use.

For developers, the central takeaway is a newly announced open multimodal model with broad visual-input ambitions and benchmark results NVIDIA says are competitive on selected tasks. Its practical value will depend on independent evaluation, workload fit, and the resources available to run it.

Step-by-Step Guide

1. 1

   Review the model documentation

   Check NVIDIA's current model information for availability, supported tasks, licensing details, and concrete hardware requirements before planning a test.

2. 2

   Confirm your GPU configuration

   Compare your available GPU generation and memory with the documented requirements, and account for precision, context length, and serving configuration.

3. 3

   Build a representative evaluation set

   Select documents, charts, tables, images, and webpages that reflect your real workflow, including examples where an incorrect answer would matter.

4. 4

   Measure quality and operating costs

   Test accuracy, latency, reliability, and cost on your workload, then compare results with the models and systems you already use.

Key Statistics

• Approximately 26 trillion pretraining and continued-pretraining tokensNVIDIA reports this as the scale of Nemotron 3.5 Super VL's pretraining and continued-pretraining.
• 3.8 million post-training samplesNVIDIA reports this figure for the model's post-training stage; it is a description of training scale, not an application performance guarantee.
• Roughly 100 open environments used for reinforcement learningNVIDIA reports using these environments during reinforcement learning for Nemotron 3.5 Super VL.

Frequently Asked Questions