Nemotron Nano 12B VL

by NVIDIA

Nemotron Nano 12B VL is an open-weight 12B multimodal model built for document-centric AI. It pairs the language model with the CRadioV2-H vision encoder to reason over text, images, tables, charts, infographics, and video. Trained on roughly 39.5 million samples from 270 datasets (33 text-only, 176 text-image, 61 video-text) totaling about 27.7 TB, the model handles document intelligence, visual question-answering, summarization, and video analysis. It can process up to four document images at high resolution alongside long text prompts, with 128K token input and output. Aimed at enterprises handling document workflows, financial analysis, legal review, and content summarization, it is ready for commercial use. It is available in NVFP4, FP8, and BF16 quantizations and is supported by vLLM, TRT-LLM, and SGLang inference engines.

Call Nemotron Nano 12B VL on Opper with the OpenAI SDK. Sign up without a credit card. Get started

Key info

Input
Output
Features
Context window
128K
Max output
8K
Input price
$0.06 /1M
Output price
$0.24 /1M
  • US residency available
  • Zero data retention on pay-as-you-go
  • No training by default
  • GDPR DPA available

Available routes

Nemotron Nano 12B VL runs on 2 different routes through the Opper gateway. Compare residency, zero data retention and training posture at a glance, with full data-handling detail per route below.

ProviderRegionZero data retentionTrainingInputOutputCache read
USNo$0.06$0.24Input price
USNo$0.20$0.60Input price

Uptime and availability

One of Nemotron Nano 12B VL's 2 routes is on the monitoring board, so the figure below is that provider's availability rather than the model's.

100%route uptime, last 30 days
Its other routes aren't measured here yet.

Name a second model in the same request and the gateway tries it on retriable errors, so a busy hour never has to reach your users. Set up a fallback chain.

Measured over the last 30 days from each provider's official status feed via StatusGator. Refreshed hourly. 1 further route is not on the board yet. See uptime for every provider Opper monitors.

Data handling per route

Each route hosting Nemotron Nano 12B VL has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

AWS Bedrock โ€” United States๐Ÿ‡บ๐Ÿ‡ธ

Zero data retention: nothing is logged, held for abuse monitoring or used for training. No training on customer data. US; SCCs; DPA available.

Zero data retention
Yes. Nothing is logged, held for abuse monitoring or used for training.
Training
No training on customer data.
Logging
None
Abuse monitoring
Always on, keeps nothing
GDPR DPA
DPA available
Transfer mechanism
SCCs

DigitalOcean โ€” United States๐Ÿ‡บ๐Ÿ‡ธ

Zero data retention with abuse monitoring: nothing is logged or used for training; content flagged by abuse monitoring is held for review. No training on customer data. US; dpf sccs; DPA available.

Zero data retention
With abuse monitoring, flagged content held.
Training
No training on customer data.
Logging
None
Abuse monitoring
Always on, holds flagged content, stored in United States
Caching
Content cached for replay
Subprocessor access
No subprocessor reads content
GDPR DPA
DPA available
Transfer mechanism
dpf sccs

Get started

Call Nemotron Nano 12B VL through the Opper gateway with one API key. Opper is drop-in compatible with the OpenAI, Anthropic and Google AI SDKs.

Nemotron Nano 12B VL is a premium model on Opper. Sign up needs no credit card: you get an API key straight away and the free models work in the playground and the API. Add a card to use premium models, pay-as-you-go with no minimum.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.OPPER_API_KEY,
baseURL: "https://api.opper.ai/v3/compat",
});
const completion = await client.chat.completions.create({
model: "nemotron-nano-12b-v2-vl",
messages: [{ role: "user", content: "Hello" }],
});
console.log(completion.choices[0].message.content);

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