Nvidia Nemotron Nano 9B V2 Base
NVIDIA's Nemotron Nano 9B V 2 Base is a large language model designed for text completion, featuring a hybrid Mamba-2 and MLP architecture with a context length of 128K. It is particularly suited for developers and researchers building LLMs, supporting multiple languages including English, Spanish, and Chinese. With 8.89B parameters, this model was pruned and distilled from a larger 12B version, showcasing its efficient design.
Input
Output
Context
128K
Max Output
16K
Parameters
9B
Technical Specifications
Input Modalities
Output Modalities
Capabilities
Loading capabilities…
Resources & Links
Estimated GPU requirements for Nvidia Nemotron Nano 9B V2 Base
Estimates 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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Frequently asked questions about Nvidia Nemotron Nano 9B V2 Base
Answered from the entry's own fields: context, license, modalities, evidence, serving and the memory to self-host.
Nvidia Nemotron Nano 9B V2 Base advertises a context window of 128,000 tokens, with a maximum output of 16,384 tokens in a single response. The figure is the creator's published maximum; a given host may serve less, and the gateway routes on what each host actually serves.
Yes. Nvidia Nemotron Nano 9B V2 Base 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 Nano 9B V2 Base accepts Text 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 Nano 9B V2 Base 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.
Not on the managed pool today; Nvidia Nemotron Nano 9B V2 Base is listed for reference and comparison. Connect your own provider key or endpoint that serves it and the gateway runs it on your account, with routing decisions recorded the same way.
About 9.2 GB at INT8 for the weights and a default context, from the catalog's 9B 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.
Fields collected from public registries, host APIs and benchmark publishers, each tagged with its source.
Last updated: Aug 28, 2026
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