NVIDIA: Nemotron 3 Nano 30B A3B
Access Nemotron 3 Nano 30B A3B from NVIDIA using Puter.js AI API.
Get Started// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain quantum computing in simple terms", {
model: "nvidia/nemotron-3-nano-30b-a3b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain quantum computing in simple terms", {
model: "nvidia/nemotron-3-nano-30b-a3b"
}).then(response => {
document.body.innerHTML = response.message.content;
});
</script>
</body>
</html>
# pip install openai
from openai import OpenAI
client = OpenAI(
base_url="https://api.puter.com/puterai/openai/v1/",
api_key="YOUR_PUTER_AUTH_TOKEN",
)
response = client.chat.completions.create(
model="nvidia/nemotron-3-nano-30b-a3b",
messages=[
{"role": "user", "content": "Explain quantum computing in simple terms"}
],
)
print(response.choices[0].message.content)
curl https://api.puter.com/puterai/openai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer YOUR_PUTER_AUTH_TOKEN" \
-d '{
"model": "nvidia/nemotron-3-nano-30b-a3b",
"messages": [
{"role": "user", "content": "Explain quantum computing in simple terms"}
]
}'
Model Card
Nemotron 3 Nano 30B A3B is a 31.6B total parameter (3.2B active) hybrid Mamba-Transformer MoE model trained from scratch by NVIDIA with a 1M token context window. It offers up to 3.3x higher throughput than comparable models and supports configurable reasoning traces for both agentic and conversational tasks.
Context Window 262K
tokens
Max Output 228K
tokens
Input Cost $0.05
per million tokens
Output Cost $0.2
per million tokens
Release Date Dec 14, 2025
Model Playground
Try Nemotron 3 Nano 30B A3B instantly in your browser.
This playground uses the Puter.js AI API — no API keys or setup required.
More AI Models From NVIDIA
Nemotron 3 Ultra 550B A55B
Nemotron 3 Ultra 550B A55B is NVIDIA's open-weight frontier reasoning model with 550B total and 55B active parameters, built on a hybrid Mamba-Transformer Mixture-of-Experts architecture. It supports a 1M token context window and is designed for long-running agentic workflows, complex multi-step reasoning, and high-accuracy tasks across code, math, and science. NVIDIA reports up to 5.9x higher inference throughput than comparable open MoE models. On the Artificial Analysis Intelligence Index it scores 48, leading US open-weight models and delivering the highest non-hallucination score in its comparison set (78.7 on AA-Omniscience). Choose it for production agentic pipelines, deep document analysis, or reasoning-heavy API workloads where both accuracy and throughput matter.
ChatNemotron 3.5 Content Safety
Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B, designed to moderate both inputs and outputs of LLMs and VLMs. It classifies prompts and responses as safe or unsafe across 23 safety categories based on the Aegis v2 taxonomy, supports 12 languages, and accepts both text and image input. An optional reasoning mode provides step-by-step chain-of-thought traces explaining each decision — useful for auditing and policy tuning. Despite its 4B size, it leads external multimodal safety benchmarks including the top harmful-F1 score on VLGuard, matching or beating 8–12B models. It also supports custom operator-defined content policies enforced at inference time. Choose it for prompt and response moderation pipelines, safety evaluation of LLM outputs, or as an inference-time guardrail in enterprise AI applications requiring explainable, policy-aware content filtering.
ChatNemotron 3 Nano Omni
Nemotron 3 Nano Omni is an open multimodal model from NVIDIA that unifies text, image, video, and audio understanding in a single inference pass. Built on a 30B-parameter hybrid Mamba-Transformer Mixture-of-Experts architecture with only ~3B active parameters per token, it delivers strong reasoning at small-model inference costs. It tops six leaderboards across document intelligence (MMLongBench-Doc, OCRBenchV2), video and audio understanding (WorldSense, DailyOmni, VoiceBench), and achieves the highest throughput of any benchmarked model — open or closed — on MediaPerf's video tasks, with up to 9x higher throughput than comparable open omni models. Designed as a multimodal perception sub-agent in agentic systems, it excels at document reasoning, GUI-based computer use, speech transcription, and audio-video analysis — replacing fragmented multi-model pipelines with a single call. Supports up to 256K context with an optional reasoning mode.
Frequently Asked Questions
You can access Nemotron 3 Nano 30B A3B by NVIDIA through Puter.js AI API. Include the library in your web app or Node.js project and start making calls with just a few lines of JavaScript — no backend and no configuration required. You can also use it with Python or cURL via Puter's OpenAI-compatible API.
Yes, it is free if you're using it through Puter.js. With the User-Pays Model, you can add Nemotron 3 Nano 30B A3B to your app at no cost — your users pay for their own AI usage directly, making it completely free for you as a developer.
| Price per 1M tokens | |
|---|---|
| Input | $0.05 |
| Output | $0.2 |
Nemotron 3 Nano 30B A3B was created by NVIDIA and released on Dec 14, 2025.
Nemotron 3 Nano 30B A3B supports a context window of 262K tokens. For reference, that is roughly equivalent to 524 pages of text.
Nemotron 3 Nano 30B A3B can generate up to 228K tokens in a single response.
Yes — the Nemotron 3 Nano 30B A3B API works with any JavaScript framework, Node.js, or plain HTML through Puter.js. Just include the library and start building. See the documentation for more details.
Get started with Puter.js
Add Nemotron 3 Nano 30B A3B to your app without worrying about API keys or setup.
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