Nvidia Nemotron 3 Nano 30B A3B
Neural Network
Nemotron 3 Nano 30B A3B is a compact NVIDIA MoE model for developers building specialized agentic systems.
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How it works Nvidia Nemotron 3 Nano 30B A3B?
Nemotron 3 Nano 30B A3B is a compact NVIDIA language model with MoE architecture: only a portion of parameters are active, achieving high computational efficiency while maintaining accuracy. NVIDIA describes it as a foundation for developers building specialized agentic systems where resource efficiency at every step is crucial. Via BotHub, the model works from Russia without a VPN or foreign card; payment is in rubles via Russian cards on a pay-as-you-go basis, with no subscription, and the balance does not expire. Requests go through a unified OpenAI-compatible API, allowing you to switch to any of over 250 models without rewriting integrations. Dialogues are encrypted via AES-GCM, and companies have access to contracts, invoices, electronic document management, and an admin panel with limits. It is useful for building agents for internal processes, creating chains of calls with predictable costs, testing prototypes before moving to a heavier model, or comparing Nemotron responses with other models in one window.Frequently asked questions about Nvidia Nemotron 3 Nano 30B A3B
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
This is an NVIDIA text model with a 262,144 token context. It analyzes large documents and codebases, writes and refactors code, helps with debugging, solves logic and STEM problems, and extracts data from images and files. Suitable for assistants, analytics, and automation via API.
The output limit is 235,929 tokens per response, meaning the model can generate very voluminous text: long documentation, a full project analysis, a large code module, or a detailed report in its entirety, without manually splitting the task into dozens of small requests.
Yes, a reasoning mode is included in the model's capabilities. Before the final answer, it works through the solution step-by-step, which significantly helps with mathematics, logic, engineering calculations, and analyzing complex code, where correctness of each intermediate step is more important than speed.
Yes, the model supports function calling and structured JSON output. You describe the tools, and it decides which function to call and with what arguments. This is convenient for agents, parsing data according to a schema, and integrations with your services and databases.
The model accepts images and documents as input: you can provide an interface screenshot, diagram, table, or PDF and ask it to analyze the content. There is no note in our data regarding audio and video input. The model returns text responses; it does not generate images or sound.
The NVIDIA model works with text in a general sense, and we make no claims about quality for specific languages. It is easier to test it on your task: in BotHub, payment is pay-as-you-go for tokens, and you can compare the result with another model in the same window.