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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsNemotron 3.5 Super VL is an open, customizable vision-language model with 120 billion total parameters and 12 billion active parameters per forward pass. Its documented NVIDIA interface accepts text, images, and video, then returns text. It is designed for document analysis, coding, search, tool-assisted workflows, and agent applications, but it does not accept audio and its deployment requirements are far beyond those of an ordinary desktop model. The practical question is not whether it can process rich inputs; it is whether your hardware, tools, and evaluation process can make that capability dependable.
Nemotron 3.5 Super VL at a glance
| Capability or property | What is established | What it means for a user |
|---|---|---|
| Accepted input | Text, images, and video in the documented NIM interface | You can submit screenshots, scanned pages, charts, tables, multipage documents, and video content alongside prompts. |
| Returned output | Text | Do not assume native image or video generation from this interface. |
| Audio | Not accepted by the documented NIM interface | An audio file or microphone stream must be transcribed by another system before the model can use it. |
| Model size | 120B total parameters; 12B active parameters per forward pass | Mixture-of-experts routing reduces active computation, but serving still requires substantial memory and parallel infrastructure. |
| Context | Up to 1,048,576 tokens in packaged NIM profiles | Very large document or code collections can fit in one request when the selected profile and hardware support that limit. |
| Architecture | Nemotron-H hybrid Mamba-2/Transformer language backbone, C-RADIO vision encoder, vision-language connector, and temporal video embedder | The components are specialized for long text, visual interpretation, and temporal video information rather than text-only completion. |
| Serving stack | NVIDIA NIM with a vLLM 0.31.0 backend and GPU-specific packaged profiles | Installation and performance depend on the exact profile, GPU memory, tensor parallelism, and serving configuration. |
| License | OpenMDW-1.1, described by NVIDIA as suitable for commercial or non-commercial use | Check the license terms and your deployment obligations before shipping a product. |
What it can see and understand
Images, screenshots, and scanned pages
The model is trained for visual-language work rather than simple image captioning. NVIDIA identifies optical character recognition, charts, tables, multipage documents, and video understanding among its training targets. In practical terms, a prompt can ask it to read a screenshot, extract fields from a scan, explain an interface state, or combine visual evidence with written instructions.
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OCR quality still depends on the source image. Small text, blur, unusual fonts, skewed scans, compression artifacts, and handwriting can produce incorrect extraction. Treat a response as an interpretation that needs checking when the consequence of one wrong character is high, such as an invoice number, legal clause, dosage, or configuration value.
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Charts and tables
Nemotron 3.5 Super VL is intended to reason over visual structure, not merely transcribe labels. NVIDIA says training includes charts and tables and that the model can connect a chart with a table elsewhere in a document. That enables questions such as identifying the quarter with the largest increase, reconciling a plotted series with its tabular values, or explaining a trend while citing the underlying rows.
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Chart questions remain a good place to require an auditable answer. Ask for the values used, the calculation, and any ambiguity in the axes or legend. A model can describe a visual trend correctly while misreading a logarithmic scale, stacked series, truncated axis, or color mapping. The published CharXiv result of 80.8% is a benchmark score, not a promise that every chart in your reports will be interpreted correctly.
Multipage documents
The model can work across pages instead of treating each image as an isolated caption. A useful document prompt should identify the desired output, request page references or quoted evidence, and distinguish text that was read directly from text that was inferred. For a long report, start with a structured extraction pass, then ask targeted questions against the extracted sections. This makes omissions easier to detect than asking for an unrestricted summary once.
Video understanding: what the temporal component adds
A temporal video embedder is part of the architecture, and NVIDIA lists video understanding as a supported use. The model can therefore reason about sequences rather than only a single still frame: for example, describing a procedure, locating when an event occurs, or relating an action to an on-screen document.
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The documented material does not establish a universal maximum duration, frame rate, resolution, or accuracy level for every video. Those limits depend on the serving profile and how the video is represented in a request. Before relying on it for surveillance, quality control, or safety review, measure performance on your own footage and define what counts as a missed event. A text answer about a video is not a frame-accurate record unless your application captures and verifies timestamps separately.
Agent and tool-use abilities
Supported workflow types
NVIDIA describes the model as intended for coding, search, tool-assisted workflows, document question answering, and image and video understanding. The NIM onboarding path documents tool calling and Model Context Protocol integration. In an agent loop, Nemotron can inspect a visual or textual request, decide which declared function to call, read the returned result, and produce the next step in text.
Tool access is not the same as autonomous reliability. Your application must define the available tools, validate arguments, enforce permissions, handle timeouts, and decide when a human must approve an action. Keep tools narrow and explicit: a function that reads a ticket is safer to expose than a function that executes arbitrary shell commands. Log the original prompt, visual inputs, tool arguments, tool results, and final response so failures can be reproduced.
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Reasoning control
Reasoning can be enabled or disabled through the chat template. Disabling it may reduce latency or simplify responses for straightforward extraction; enabling it can help with multistep coding, chart calculations, and tool selection. The setting is a workflow choice, not a guarantee of correctness. Evaluate both modes on representative tasks, including cases where the model must refuse an unsafe or unsupported action.
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NVIDIA reports training on repositories, issue and pull-request pairs, commit diffs, build and continuous-integration logs, and agent trajectories. That background is relevant to code review, debugging, change planning, and CI-log diagnosis. It does not mean the model can compile or test code by itself. Give it access to a controlled build and test tool, return the actual output, and require it to separate observed failures from proposed fixes.
How to interpret the published scores
| Evaluation | Published result | How to use the number |
|---|---|---|
| CharXiv chart reasoning | 80.8% | A signal of chart-reasoning ability on that benchmark; it is not an accuracy guarantee for your charts. |
| Python code interpretation | 84% | Evidence of performance on the named interpretation evaluation, not proof that generated code is safe or executable. |
| Hermes Bench | 63.44% | A benchmark result for the evaluated setting; it should not be converted into a general autonomy percentage. |
| LangChain behavioral evaluation | 87.05% | Evidence from the reported behavioral test, with results still dependent on prompts, tools, and harness configuration. |
| GPQA-Diamond | 89.52% in BF16; 89.90% in NVFP4 | Published API Catalog results for two numerical formats, not a prediction of factuality in production. |
These figures are reported by NVIDIA or in NVIDIA’s API Catalog in 2026. They come from named datasets and evaluation setups. None establishes reliable autonomy, universal factuality, or successful tool execution in an untested environment. Reproduce the tasks that matter to your organization, measure both correct and harmful actions, and include adversarial inputs and incomplete documents.
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Context length and architecture in practical terms
The packaged NIM profiles specify a maximum context of 1,048,576 tokens. That is an upper bound for supported profiles, not a requirement to place a million tokens into every request. Huge contexts increase memory use, processing time, and the chance that relevant evidence is buried among irrelevant material. A retrieval or staged-analysis design can be faster and easier to audit even when the model supports the full limit.
The language backbone combines Mamba-2 and Transformer components in the Nemotron-H design. A C-RADIO vision encoder turns visual input into representations, a vision-language connector joins those representations to the language model, and a temporal embedder handles video information. The mixture-of-experts design contains 120 billion total parameters while activating 12 billion per forward pass. Active-parameter counts should not be mistaken for the total memory needed to load weights, caches, runtimes, and visual inputs.
Running it locally versus using a hosted service
What local deployment requires
NVIDIA documents local execution on DGX Spark as an optimization target. That documentation is not evidence that every consumer laptop or desktop can run the model. A local installation must fit the model weights and runtime in available GPU memory, and may require multiple GPUs connected for tensor parallelism. The correct NIM profile, quantization format, driver stack, and serving configuration all affect whether the model starts and how quickly it responds.
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NVFP4 and serving optimizations
The NVFP4 release uses quantization-aware distillation, supports up to one million tokens, and includes native Multi-Token Prediction speculative decoding. Quantization can reduce memory pressure and improve throughput, but it can also change numerical behavior. Compare the quantized profile with the precision you intend to use on your own document, chart, coding, and tool-call tests rather than assuming that a published score transfers unchanged.
NIM and vLLM details
The NIM backend is vLLM 0.31.0, with packaged profiles validated for particular GPUs. A profile validated on one accelerator is not automatically portable to another. Plan capacity around concurrent requests, visual-token volume, context size, and generation length, then verify latency under realistic load. For a local proof of concept, begin with a small set of representative images, documents, and videos before increasing concurrency or context.
A practical evaluation plan before production
- Define the input mix. Collect real screenshots, scans, charts, tables, multipage files, and representative video clips. Record resolution, language, page count, and typical corruption such as blur or skew.
- Set an evidence standard. Decide when an answer must quote text, identify a page or timestamp, show a calculation, or request human review.
- Test modality boundaries. Confirm that text, image, and video requests work in your selected NIM profile, and route audio through a separate transcription component because the documented interface does not accept audio.
- Exercise tools safely. Use mocked or read-only functions first. Check argument validation, permission failures, timeouts, duplicate calls, and malformed tool results.
- Measure both quality and operations. Track extraction accuracy, chart-answer accuracy, missed events, refusal quality, time to first token, total latency, memory use, and throughput at your expected concurrency.
- Test long context deliberately. Compare a full-document request with staged retrieval. Verify that the answer cites the correct page or section instead of relying on a plausible but irrelevant passage.
- Choose a deployment profile. Select precision, quantization, GPU count, and tensor-parallel settings only after the workload tests. Document the exact profile so results can be reproduced after an upgrade.
What it cannot do, and assumptions to avoid
- It cannot take audio directly through the documented NIM interface. NVIDIA’s wording is explicit: the interface accepts text, image, and video input and returns text; it does not accept audio input.
- It is not established as an image or video generator. The cited interface documents text output. Use a separate generation system if your product must create pixels or rendered video.
- It is not automatically an autonomous operator. Tool calling and MCP integration provide mechanisms for action, while your software remains responsible for authorization, validation, retries, and human approval.
- It is not proven on every document or video condition. Published benchmark scores describe particular datasets and settings. They do not remove the need for domain testing, especially for small text, unusual charts, handwriting, low-light footage, or safety-critical decisions.
- It is not lightweight simply because only 12B parameters are active. The 120B total model, visual encoders, context cache, and serving stack create substantial infrastructure requirements.
- Partner performance claims are workflow-specific. NVIDIA reports 40-times lower inference cost and five-times lower latency for a customized vendor-credit workflow, plus up to 50% higher throughput and 10-times lower latency for a receipt-processing workflow. Those are partner-reported results for customized systems, not general guarantees.
When Nemotron 3.5 Super VL is a good fit
Choose it when one system must combine visual documents or video with long textual context, coding or reasoning, and controlled tools. It is especially compelling for teams that need to customize an open model, operate through NVIDIA’s serving stack, and can provide the GPU infrastructure required by the selected profile.
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Bottom line
Nemotron 3.5 Super VL is a serious vision-language agent foundation: it reads text and visual documents, reasons over charts and tables, processes video, writes and analyzes code, and connects to tools through NIM. Its boundaries are equally important. The documented interface has no audio input and returns text rather than generated media; benchmark scores are contextual; and local operation requires specialized, profile-matched infrastructure. Treat it as a powerful, testable component in a controlled agent system—not as an all-purpose autonomous operator that will work unchanged on any computer.
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