NVIDIA Nemotron 3.5 Super VL 120B A12B is a vision-language model built by NVIDIA for tasks like reasoning, coding, and document understanding. It is a hybrid Mixture of Experts model combining Mamba-2 and attention layers with a C-RADIO vision encoder, featuring 120B total parameters and 12B active parameters.
Input
Output
Context
262K
Max Output
-
Parameters
124.5B
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Output Modalities
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The same model in other encodings or serving configurations. Requesting nvidia-nemotron-3-5-super-vl-120b-a12b lets routing pick among them; the ids below pin one build.
nvidia-nemotron-3-5-super-vl-120b-a12b:fp4quantizedEstimates based on INT8 quantization at up to 32K context. A count above one assumes tensor parallelism across the cards. Actual requirements vary by framework and configuration.
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Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
NVIDIA Nemotron 3.5 Super VL 120B A12B advertises a context window of 262,144 tokens. The figure is the creator's published maximum; a given provider may serve less, and the gateway routes on what each provider actually serves.
Yes. NVIDIA Nemotron 3.5 Super VL 120B A12B is an open-weight model released under the Other license, so the weights can be downloaded and self-hosted within that license's terms.
NVIDIA Nemotron 3.5 Super VL 120B A12B accepts Text and Image and produces Text. The capabilities card on this page lists which API features each deployment honours, such as function calling and structured output, with the source each was checked against.
No published benchmark result for NVIDIA Nemotron 3.5 Super VL 120B A12B is in the catalog yet, so the model is shown as unmeasured. It is not ranked or estimated; the router treats it as unknown for every task until a suite measures it.
Yes. NVIDIA Nemotron 3.5 Super VL 120B A12B is served on the managed pool through the OpenAI-compatible endpoint as nvidia-nemotron-3-5-super-vl-120b-a12b, pinned by name or chosen by routing when it is the best fit for a request. The Try in Playground button opens it directly.
About 123.0 GB at INT8 for the weights and a default context, from the catalog's 124.5B parameter count; FP16 needs roughly twice that, and long contexts or many concurrent requests add KV cache on top. The GPU section on this page lists cards that hold it, and the capacity planner sizes it for your context length and traffic.
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Last updated: Nov 6, 2026
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