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Nemotron 3.5 Super VL is NVIDIA’s 120-billion-parameter vision-language mixture-of-experts model, with about 12 billion parameters active for each token. It accepts text, RGB images, and sampled video frames, then produces text for document analysis, visual reasoning, coding, tool use, and multimodal agent workflows.
You can use the model commercially under the OpenMDW-1.1 license, and NVIDIA documents local serving with vLLM, SGLang, and NVIDIA NIM. The headline context window is up to 1,000,000 tokens (1,048,576 in the packaged NIM profile), but sustaining that length depends on GPU memory, precision, runtime, concurrency, and the amount of visual data in a request.
What is Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL is a multimodal model developed by NVIDIA Corporation. Its architecture combines interleaved Mamba-2 layers, mixture-of-experts (MoE) layers, attention, a C-RADIO v4-H vision encoder, and a vision-language connector. The design uses a large total parameter count while activating a smaller subset for each token, which is why NVIDIA describes it as a 120B-total/12B-active model.
The model is aimed at workloads that need both language and visual understanding:
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- Extracting and reasoning over documents, charts, screenshots, and photographs.
- Analyzing video by processing sampled frames.
- Writing, reviewing, and debugging code.
- Calling tools and participating in multimodal agent workflows.
- Handling very long text histories or document collections.
It is not an image generator. The documented output is text, even when the input includes images or video.
How many tokens can it handle?
NVIDIA advertises a maximum context of up to 1,000,000 tokens. NVIDIA’s NIM deployment guide gives the corresponding packaged-profile limit as 1,048,576 tokens. Those figures describe a configured maximum, not a guarantee that every installation can process a million-token request at acceptable speed.
Why the advertised maximum is not the practical maximum
Context consumes memory for the prompt, generated output, multimodal embeddings, and the model’s Mamba and attention state. The usable limit therefore changes with:
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- GPU model and total available memory.
- BF16 versus NVFP4 precision.
- Number of simultaneous requests.
- Maximum output-token setting.
- How many images or video frames are included.
- Runtime configuration and cache settings.
NVIDIA publishes explicit one-million-token vLLM recipes for a four-H100 profile and a two-GB200 profile. A smaller workstation may run the model with a shorter context, lower concurrency, or a quantized checkpoint, but the million-token setting should be treated as a reference deployment target rather than a universal desktop requirement.
Planning a long-context deployment
- Start with the GPU profile documented for your hardware rather than setting the maximum context immediately.
- Set the context limit, maximum output, and request concurrency together; increasing all three multiplies memory pressure.
- Measure first-token latency and tokens per second with representative text and visual inputs.
- Increase context in stages while watching for out-of-memory errors, excessive swapping, or unacceptable latency.
- Reserve headroom for image embeddings, video-frame buffers, and the runtime’s cache.
Is Nemotron 3.5 Super VL commercially usable?
Yes. NVIDIA’s model card says: “This model is ready for commercial or non-commercial use.” The card lists OpenMDW-1.1 as the license and identifies the OpenMDW License Agreement, version 1.1, as the governing download terms.
What the license decision means in practice
Commercial permission does not eliminate compliance work. Before distributing a product or a modified checkpoint, read the complete OpenMDW-1.1 agreement and determine which obligations apply to your use. The agreement addresses notices, derivatives, redistribution, and use restrictions. Keep the license text and required notices with internal deployment records and with any redistribution package that requires them.
The model card is a useful summary, but it is not a substitute for reviewing the agreement with counsel when the model is embedded in a paid service, redistributed to customers, fine-tuned, or combined with other components. Also check the licenses of the serving framework, container images, data, and any tools connected to an agent; those terms are separate from OpenMDW-1.1.
Can you run it locally?
Yes. NVIDIA documents local inference with vLLM and SGLang, and offers NVIDIA NIM as a packaged production path. The model card lists Linux as the operating system and NVIDIA Hopper and Blackwell hardware families as supported reference platforms.
Documented reference hardware
| Deployment target | Documented configuration | What it establishes |
|---|---|---|
| vLLM or SGLang local serving | 4× NVIDIA H100 or 2× NVIDIA GB200 | NVIDIA’s reference profile for text, image, and video serving |
| NVIDIA NIM packaged profile | Profile-dependent; the guide documents settings up to 1,048,576 tokens | A validated microservice path with packaged engine and multimodal settings |
| RTX, workstation, Jetson, DGX Spark, data-center, and cloud systems | Supported product families are described by NVIDIA, but no single GPU requirement is stated for every system | Availability across families does not mean every card can hold the 120B BF16 checkpoint or a full million-token workload |
The four-H100 and two-GB200 figures are reference deployments, not a claim that one H100 or one consumer RTX card can run the full BF16 model at the advertised context. On smaller systems, you may need NVFP4, fewer concurrent requests, a shorter context, or a different model size.
Which runtime should you choose?
| Option | Best fit | Important capabilities | Trade-offs to evaluate |
|---|---|---|---|
| vLLM | Teams wanting a widely used self-hosted serving engine and NVIDIA’s documented BF16 recipe | Tensor parallelism, multimodal limits, reasoning and tool-call parsers, Mamba-cache settings, and native multi-token prediction (MTP) speculative decoding | You must maintain the container, model files, GPU drivers, and compatible runtime versions |
| SGLang | Operators whose serving stack already uses SGLang or who prefer its scheduling and serving controls | NVIDIA documents it as a local serving option for the model | Validate the exact model support, multimodal behavior, parser configuration, and performance for your release |
| NVIDIA NIM | Production teams that want a packaged microservice with validated defaults | Packages engine arguments, vLLM 0.31.0, context settings, Mamba-cache settings, chat templates, video decoding, parsers, and speculative decoding | Less low-level control than assembling the stack yourself; confirm the NIM profile and infrastructure requirements for your deployment |
| Hosted inference | Users who do not want to provision or maintain multi-GPU hardware | Provider-managed capacity and upgrades | Check the provider’s actual context limit, image/video support, concurrency, data-retention policy, latency, price, and OpenMDW-1.1 compliance responsibilities |
Choose by measured workload rather than the model name alone. A hosted endpoint may be faster to launch, while local serving gives you control over data locality, networking, model files, and runtime versions. For either route, verify that the provider or your own deployment actually supports video and the context size your application sends.
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BF16 or NVFP4?
BF16
BF16 is NVIDIA’s full-precision reference release. It is the sensible baseline for customization, domain adaptation, and full-precision evaluation. It generally requires the most memory, making the documented multi-GPU profiles especially relevant.
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NVFP4
NVFP4 is the quantized release intended for optimized deployment. NVIDIA Research reports that the released NVFP4 checkpoint recovered 99.62% of median BF16 accuracy across 36 evaluated benchmarks. This is an NVIDIA-reported result from specified evaluation setups, not an independent test or a guarantee for your prompts.
Quantization can make a deployment fit within a smaller memory budget or leave more room for context and concurrency, but quality and speed depend on the runtime, hardware, and task. Test your own document types, visual inputs, tool calls, and long prompts before switching a production service from BF16.
Speculative decoding with MTP
Native Multi-Token Prediction (MTP) enables speculative decoding: a draft component proposes several tokens and the main model verifies them. NVIDIA supplies optimized inference recipes and standalone speculative drafters, but the useful draft-token count varies by hardware and serving engine. Treat MTP settings as hardware-specific tuning parameters, not universal defaults.
Does it understand images and video?
Yes. Inputs include text, RGB images, and sampled video frames. The model card documents MP4 video of up to two minutes; the serving examples show configurations with one image and one video per request. Output remains text.
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What video support actually involves
The model does not consume an unlimited continuous stream. The serving layer decodes the MP4 and samples frames, so frame sampling, resolution, and the number of visual tokens affect both memory use and reasoning detail. A two-minute file is a documented input ceiling for the model card’s configuration, not a promise that every runtime or profile will accept every codec, frame rate, or resolution.
For reliable applications, validate the entire path: file decoding, frame sampling, multimodal request limits, prompt template, timeout, and output length. A deployment configured for text-only requests may appear healthy while rejecting or truncating visual inputs.
How to plan a local deployment
- Confirm the environment. Use Linux and a supported NVIDIA Hopper or Blackwell platform for the documented reference path. Check driver, CUDA, container, and framework compatibility before downloading the checkpoint.
- Select a hardware profile. Use four H100 GPUs or two GB200 systems when you need the documented full multimodal reference configuration. For RTX, Jetson, DGX Spark, or other systems, plan around the memory available on the exact SKU rather than the product family name.
- Choose the checkpoint. Start with BF16 for evaluation or adaptation. Choose NVFP4 when memory, throughput, or deployment density matters and validate quality on your workload.
- Choose the serving layer. Use the documented vLLM or SGLang path for direct control, or NIM when you want NVIDIA’s packaged arguments, templates, parsers, and decoding components.
- Configure parallelism and limits. Set tensor parallelism for the number of GPUs, then define maximum context, output tokens, concurrent requests, and image/video-per-request limits.
- Enable multimodal and agent features. Configure the chat template, reasoning parser, tool-call parser, video decoding, and MTP speculative decoding where supported by your chosen engine.
- Load-test progressively. Test text-only prompts first, then one image, then video, and finally long mixed requests. Record memory use, first-token latency, generation speed, error rates, and quality.
- Lock the production configuration. Record the model revision, precision, runtime version, GPU type, context limit, parser settings, and license notices so a later upgrade can be audited.
What performance numbers are documented?
NVIDIA’s model card reports a 51.78% SWE-bench Pro score, evaluated by NVIDIA. NVIDIA Research reports the 99.62% median BF16-accuracy recovery for the NVFP4 checkpoint across 36 benchmarks. These numbers are useful reference points, but they are vendor-reported results from specified evaluation setups. They are not independent comparisons and do not predict latency, accuracy, or cost for your application.
For purchasing or capacity decisions, measure the dimensions that matter to your service: sustained tokens per second, first-token latency, visual preprocessing time, maximum concurrent sessions, failure rate at the target context, and quality on your own documents and videos.
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“The model loads, but a million-token request fails.”
Reduce concurrency, output length, or visual payload first. Then check the configured context limit and cache settings. The advertised maximum requires an appropriate multi-GPU profile and does not automatically transfer to a smaller machine.
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“BF16 runs out of memory on my workstation.”
Use the NVFP4 release if it is compatible with your runtime, shorten the context, reduce simultaneous requests, or move to the documented multi-GPU hardware. Do not assume that an RTX family label identifies enough memory for this 120B-total checkpoint.
“Text works but images or video are rejected.”
Verify that the multimodal model variant, chat template, image/video limits, and video decoder are enabled. Confirm that the file is an MP4 within the documented two-minute limit and that your engine supports the model’s visual request format.
“Tool calls are emitted as ordinary text.”
Enable the runtime’s tool-call parser and use the matching chat template. Parser names and arguments are engine- and version-specific, so copy the settings for the exact vLLM, SGLang, or NIM release you deploy.
“Quantized output is different from BF16.”
Benchmark representative tasks before rollout. The 99.62% figure is a median recovery across 36 NVIDIA-evaluated benchmarks; it does not mean every prompt, visual task, or tool call will match BF16 exactly.
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