Nemotron 3.5 Super VL is NVIDIA’s 120-billion-parameter hybrid Mamba-Transformer mixture-of-experts vision-language model, with 12 billion parameters active for each token. It accepts text, RGB images, and sampled video frames, and can produce text for document analysis, visual reasoning, coding, tool use, and multimodal agents. NVIDIA advertises up to a one-million-token context, commercial and non-commercial use under OpenMDW-1.1, and local serving with vLLM or SGLang. In practice, the model’s usable context, speed, and hardware requirements depend heavily on precision, GPU memory, concurrency, media resolution, and the serving profile.
What is Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL is a multimodal large language model developed by NVIDIA Corporation. Its design combines interleaved Mamba-2 layers, attention, a mixture-of-experts (MoE) language model, a C-RADIO v4-H vision encoder, and a vision-language connector. That combination is intended to provide long-context sequence handling while retaining attention-based capabilities for detailed reasoning over visual and textual information.
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The model accepts three input types:
- Text prompts and conversation history.
- RGB still images.
- Sampled frames from video, including MP4 video of up to two minutes in the documented model-card examples.
Its output is text. Typical workloads include extracting fields from documents, answering questions about charts or photographs, analyzing video events, writing or reviewing code, invoking tools, and coordinating multimodal agent workflows.
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At-a-glance specifications
| Specification | Documented value or behavior |
|---|---|
| Developer | NVIDIA Corporation |
| Architecture | Hybrid Mamba-2 and Transformer, MoE language model, C-RADIO v4-H vision encoder, vision-language connector |
| Total / active parameters | 120B total / 12B active |
| Advertised maximum context | Up to 1,000,000 tokens |
| Packaged NIM context limit | 1,048,576 tokens |
| Inputs | Text, RGB images, sampled video frames |
| Output | Text |
| Documented video example | MP4, up to two minutes |
| Reference local deployment | 4× H100 or 2× GB200 |
| Operating system and GPU families | Linux; NVIDIA Hopper and Blackwell are identified as supported families |
How many tokens can it handle?
NVIDIA describes the model as supporting up to 1,000,000 tokens. NVIDIA’s NIM packaging specifies an exact maximum of 1,048,576 tokens. The two figures describe the same one-million-token class of context, with the latter being the packaged profile’s power-of-two limit.
That number is a configured upper bound, not a guarantee that every installation can process a million tokens at useful speed. The practical limit changes with:
- GPU memory: weights, Mamba state, attention or key-value caches, vision features, and framework overhead all consume memory.
- Precision: BF16 generally requires more memory than NVFP4.
- Concurrency: several simultaneous requests divide memory and compute that one request could otherwise use.
- Media: images and video frames add encoded tokens or features, and higher resolution or more sampled frames increase the burden.
- Serving profile: runtime defaults, cache settings, and parallelism can impose a lower operational ceiling than the model’s advertised maximum.
NVIDIA provides explicit one-million-token vLLM recipes for 4× H100 and 2× GB200 profiles. Treat those as reference configurations rather than proof that a different GPU count, precision, or workload will sustain the same length. For production, test the longest prompt your application actually sends, then measure latency, memory headroom, and failure behavior at expected concurrency.
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- Reserve room for the model’s response, tool calls, and any system instructions; do not allocate the entire limit to user content.
- Estimate the token cost of every image and sampled video frame in addition to text.
- Start with a lower context setting in your runtime and increase it while monitoring memory and time-to-first-token.
- Load-test with realistic simultaneous requests rather than a single benchmark prompt.
Does it understand images and video?
Yes. The C-RADIO v4-H encoder converts visual information for the language model, while the connector aligns those features with the text-generation stack. The documented interface accepts RGB images and sampled video frames. NVIDIA’s examples show multimodal configurations allowing one image and one video in a request.
Video support is frame-based: the service decodes or samples frames and sends those visual representations to the model. A two-minute MP4 limit in the model card is a documented example boundary, not a promise that every codec, frame rate, resolution, or runtime setting will work identically. More frames provide more temporal evidence but consume more context and processing time. For long recordings, sample deliberately around the events you need analyzed and verify the decoder’s accepted format before deploying.
The model returns text, so applications that need structured actions should enforce a schema in the prompt or tool interface and validate the result before executing it. Visual understanding can identify text, objects, layouts, and events, but the accuracy of small print, unusual diagrams, rapid motion, or heavily compressed footage depends on the supplied media and the deployment configuration.
Is Nemotron 3.5 Super VL commercially usable?
The NVIDIA model card states: “This model is ready for commercial or non-commercial use.” It lists OpenMDW-1.1 as the license and identifies the OpenMDW License Agreement, version 1.1, as the governing terms for downloading and using the model.
Commercial readiness does not mean that every use is automatically permitted without review. Before shipping a product, read the complete OpenMDW-1.1 agreement and record how your distribution model fits its requirements. In particular, check the provisions dealing with:
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- Required notices or attribution when redistributing the model or a service built from it.
- What counts as a derivative model, modification, fine-tune, or other adaptation.
- Redistribution of weights, converted or quantized checkpoints, and packaged containers.
- Use restrictions, prohibited purposes, and any obligations that flow to downstream users.
The model card’s commercial-use statement is a useful eligibility signal, not legal advice. Keep the exact license text and the version you reviewed with your release records, and obtain legal guidance for regulated, high-risk, or redistribution-heavy deployments.
Can you run it locally, and what GPU is required?
Yes. NVIDIA documents local serving with vLLM and SGLang. The model card’s reference deployment lists 4× H100 or 2× GB200 for text, image, and video serving. Linux is the documented operating system, and NVIDIA identifies Hopper and Blackwell hardware families as supported.
These are reference configurations for the full model and multimodal serving, not a universal minimum. A workstation-class RTX system may be suitable for an optimized or quantized experiment, but the fact that NVIDIA positions the model for RTX systems does not mean every RTX card can hold the 120B BF16 checkpoint or run a one-million-token workload. Check the selected checkpoint’s memory footprint, the GPU’s available VRAM, and the runtime’s support for the model architecture before attempting a load.
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- Weight memory: BF16 stores substantially more bytes per parameter than a four-bit format, and the total checkpoint is still based on 120B parameters even though 12B are active per token.
- Runtime state: Mamba state, attention caches, MoE routing buffers, and framework allocations require additional VRAM.
- Vision workload: image embeddings and video-frame batches increase peak memory.
- Tensor parallelism: multiple GPUs divide model computation and weights, but interconnect speed affects latency and scaling.
- Headroom: leave memory available for batching, CUDA workspace, and occasional larger requests instead of running at the edge of an out-of-memory failure.
Should you use BF16, NVFP4, vLLM, SGLang, or NVIDIA NIM?
| Choice | Best fit | Important trade-off |
|---|---|---|
| BF16 checkpoint | Customization, domain adaptation, and full-precision evaluation | Higher memory use; needs a larger multi-GPU deployment for the documented reference profile |
| NVFP4 checkpoint | Optimized inference where lower memory and higher throughput matter | Quantization changes numerical behavior; validate your own workload |
| vLLM | Teams wanting a documented open serving stack with tensor parallelism, multimodal limits, parsers, and speculative decoding | You must maintain the container, model configuration, and compatibility of the selected version |
| SGLang | Teams already operating SGLang and wanting a local serving path | Confirm that the chosen release supports this model’s multimodal and Mamba/MoE features |
| NVIDIA NIM | Managed production packaging with validated engine arguments and model-specific defaults | Less low-level control than assembling the stack yourself; deployment still requires suitable GPUs and license review |
| Hosted service | Quick access without purchasing or operating multi-GPU hardware | Data locality, sustained context, concurrency, pricing, and model-version control depend on the provider |
BF16 versus NVFP4
BF16 is NVIDIA’s full-precision reference release. It is the safer starting point for evaluation, fine-tuning or other customization because it avoids the additional approximation introduced by quantization. NVFP4 is the quantized release intended for optimized deployment and lower memory pressure.
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NVIDIA Research reports that the released NVFP4 checkpoint recovered 99.62% of median BF16 accuracy across 36 evaluated benchmarks. This is a vendor-reported result from NVIDIA’s evaluation setup, not an independent guarantee. Measure document extraction, visual question answering, code generation, tool calls, and long-context retrieval on your own prompts before selecting NVFP4 for a business-critical workload.
Speculative decoding with MTP
Native Multi-Token Prediction (MTP) enables speculative decoding: a draft mechanism proposes several future tokens, and the main model verifies them. Accepted drafts can improve generation throughput without changing the final decoding target. NVIDIA supplies optimized inference recipes and standalone speculative drafters, but the number of draft tokens and other settings vary by hardware and serving engine. Tune them against your latency and acceptance-rate measurements rather than copying a setting between GPU types.
Local deployment: a practical sequence
- Confirm the license and checkpoint. Obtain the official model package, record its version, and review OpenMDW-1.1 before downloading or redistributing weights.
- Inventory the machine. Record GPU model, VRAM, GPU count, interconnect, driver, CUDA compatibility, system RAM, Linux distribution, and available storage. Compare those figures with the 4× H100 or 2× GB200 reference profile.
- Choose precision. Select BF16 for full-precision evaluation or customization. Select NVFP4 when memory and throughput are the priority, then run task-specific quality checks.
- Select a serving path. Use the NVIDIA-documented vLLM or SGLang route for self-managed serving. Choose NIM when you want NVIDIA’s packaged microservice, validated engine arguments, chat template, video decoding, parsers, and speculative-decoding integration.
- Configure parallelism and media limits. In vLLM, set tensor parallelism for the available GPUs and configure the multimodal per-prompt limits. Enable the documented reasoning and tool-call parsers when your application needs them.
- Set context conservatively. Start below one million tokens, verify a short text request, then test image and video prompts. Increase the limit only after memory and latency remain acceptable.
- Enable MTP only after baseline testing. Compare tokens per second, time-to-first-token, and output consistency with and without speculative decoding.
- Validate the API contract. Test text-only, one-image, and one-video requests; malformed media; oversized prompts; tool calls; cancellation; and concurrent requests.
- Load-test production traffic. Measure peak VRAM, queueing, latency percentiles, throughput, and error rates at the concurrency and media mix you expect.
NIM’s documented package includes vLLM 0.31.0, context settings up to 1,048,576 tokens, Mamba-cache settings, chat templates, video decoding, parsers, and speculative decoding. Use the NIM profile when you want those model-specific defaults rather than manually reproducing them.
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Common failure modes and fixes
Out-of-memory errors during startup
Reduce the context setting, switch from BF16 to NVFP4 if its quality is acceptable, lower multimodal limits, or add GPUs and configure tensor parallelism. Also check for another process consuming VRAM and leave headroom for runtime allocations.
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Text works but image or video requests fail
Verify that the server was started with multimodal support and the correct image/video limits. Confirm RGB image encoding, MP4 compatibility, frame sampling behavior, and the request schema expected by the selected runtime.
One-million-token requests are too slow
Long context increases prefill work even when generation is short. Reduce unnecessary history, summarize old turns, sample fewer video frames, or reserve the maximum context for requests that genuinely need it. Test MTP and adjust concurrency rather than assuming a larger context profile will improve throughput.
Tool calls or reasoning output are malformed
Enable the model-appropriate reasoning and tool-call parsers documented for your serving stack, use the matching chat template, and validate every tool argument before execution. A parser cannot compensate for an incompatible template or an application that assumes a different schema.
Quality drops after quantization
Compare BF16 and NVFP4 on representative documents, images, videos, code tasks, and tool calls. If a specific workflow regresses, keep BF16 for that route, adjust prompts or media preprocessing, or use NVFP4 only for workloads whose measured quality remains acceptable.
How should benchmark claims be interpreted?
NVIDIA reports a 51.78% SWE-bench Pro score for Nemotron 3.5 Super VL and the 99.62% NVFP4 median-accuracy recovery figure across 36 benchmarks. Those numbers were evaluated by NVIDIA under specified setups. They are useful reference points, but they are not independent comparative tests and do not guarantee performance on your repository, documents, camera footage, prompts, or concurrency.
For a meaningful acceptance test, keep the model version, precision, prompt template, sampling settings, media preprocessing, context limit, and hardware fixed. Report both quality and systems metrics: task accuracy, tool-call validity, time-to-first-token, tokens per second, p95 latency, peak VRAM, and failure rate.
What is the practical verdict?
Nemotron 3.5 Super VL is best understood as a large, long-context multimodal system with a relatively small active parameter count, not as a lightweight desktop model. Its one-million-token ceiling is real in NVIDIA’s documented profiles, but sustaining it requires matching hardware, careful cache and media settings, and realistic concurrency tests. OpenMDW-1.1 permits commercial and non-commercial use according to its terms, while local deployment gives organizations control over data locality and software configuration. BF16 favors fidelity and customization; NVFP4 favors memory efficiency; vLLM and SGLang favor self-managed control; and NIM favors packaged, model-specific operations.
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