Nemotron 3 Nano 30B is a production-ready, open-weight 30B-parameter Mixture-of-Experts model built on a hybrid Mamba-2 and Transformer architecture with roughly 3.5B active parameters per token. The hybrid design aims for high throughput and fast responses while keeping accuracy on par with standard transformers. The model handles mathematical reasoning, code generation, and long-context understanding, and supports tool use and reasoning modes for agentic systems. It offers 256K token context (the default Hugging Face configuration, scalable further with more memory) and works across multiple natural languages and many programming languages. Designed for developers building AI agents, retrieval-augmented generation systems, chatbots, and instruction-following applications, it suits complex problem-solving at a reduced memory footprint compared with larger dense models.
Nemotron 3 Nano 30B runs on 2 different routes through the qynio gateway. Compare residency, zero retention, and training posture at a glance — full data-handling detail per route below.
Each route hosting Nemotron 3 Nano 30B has its own privacy posture, residency, and no-KYC terms. Postures are maintained by qynio with a last-verification timestamp.
Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. EEA; wallets; deposit available.
Zero data retention is on by default on Pay-as-you-go — no action required. No training on customer data. US; wallets; deposit available.
Call Nemotron 3 Nano 30B through the qynio gateway with one API key. Let your coding agent set it up, or call it directly — qynio is drop-in compatible with the OpenAI, Anthropic, and Google AI SDKs.
Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up qynio for you.
import OpenAI from "openai";const client = new OpenAI({apiKey: process.env.QYN_API_KEY,baseURL: "https://api.qyn.io/v3/compat",});const completion = await client.chat.completions.create({model: "geodd:no/nemotron-3-nano-30b",messages: [{ role: "user", content: "Hello" }],});console.log(completion.choices[0].message.content);
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