Why does "30B total, 3B active" matter for inference cost?

You pay for compute proportional to the active parameters, not the total. Nemotron 3 Nano 30B A3B runs at speeds and costs closer to a 3B dense model but draws on 30B parameters of learned knowledge. The MoE routing mechanism selects the relevant subset per token.

How does the Mamba architecture enable the context of 262.1K tokens?

Mamba layers process sequences with linear-time complexity rather than the quadratic scaling of standard attention. That makes it practical to hold 262.1K tokens in context without the memory explosion that would make pure-attention models infeasible at that length.

How does Nemotron 3 Nano 30B A3B differ from Nemotron Nano 9B v2?

They use different architectures. Nemotron 3 Nano 30B A3B is a sparse MoE with 30B total/3B active parameters and a context window of 262.1K tokens. Nemotron Nano 9B v2 is a dense 9B model with a 128K-token context window. Choose Nemotron 3 Nano 30B A3B for throughput across multi-agent systems and Nano 9B v2 as a compact reasoning model.

Where are hosted input and output prices listed?

Current pricing is shown on this page. AI Gateway routes across providers, and rates may vary by provider.

NVIDIA

Nemotron 3 Nano 30B A3B

Nemotron 3 Nano 30B A3B is a sparse hybrid Mamba-Transformer mixture-of-experts (MoE) model with 30B total parameters but only 3B active per token. It supports a context window of 262.1K tokens with throughput closer to a 3B dense model than a 30B one.

View API reference

Input and output price: Input $0.05, Output $0.24, Per 1M tokens
24h uptime: Loading AI Gateway uptime

Get API key Read the docs

import { streamText } from 'ai'

const result = streamText({
  model: 'nvidia/nemotron-3-nano-30b-a3b',
  prompt: 'Why is the sky blue?'
})

Read docs

Overview Playground Providers Uptime Status Throughput Latency API Similar About FAQ

Copy link to headingPlayground

Try out Nemotron 3 Nano 30B A3B by NVIDIA. Usage is billed to your team at API rates. Free users (those who haven't made a payment) get $5 of credits every 30 days.

Nemotron 3 Nano 30B A3B

Copy link to headingProviders

Route requests across multiple providers. Copy a provider slug to set your preference. Visit the docs for more info. Using a provider means you agree to their terms, listed under Legal.

Provider

Context	Max Output	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	ZDR	No Training	Free Tier	Release Date

DeepInfra

Legal:Terms•Privacy

262K

0.3 s

128 tps

$0.05/M

$0.24/M

—

12/15/2025

Getting started

Call Nemotron 3 Nano 30B A3B through AI Gateway with the AI SDK generateText and streamText functions, or through the OpenAI Chat Completions, OpenAI Responses, and Anthropic Messages APIs by changing the base URL. AI Gateway authenticates the request and routes it to an available provider.

Install the AI SDK (pnpm add ai dotenv), create an API key from the API Keys page, and set it as AI_GATEWAY_API_KEY in your environment. Full setup is covered in the text generation quickstart.

index.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'nvidia/nemotron-3-nano-30b-a3b',
    prompt: 'Why is the sky blue?',
  });

  console.log(result.text);
}

main().catch(console.error);

Top-level parameters

The same Nemotron 3 Nano 30B A3B request in each API format AI Gateway supports.

top-level-params.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'nvidia/nemotron-3-nano-30b-a3b',
    system: 'You are a concise technical assistant.',
    prompt: 'Summarize the tradeoffs between static generation and SSR.',
    maxOutputTokens: 1024,
  });

  console.log(result.text);
}

main().catch(console.error);

Standard parameters like prompt, messages, temperature, and tools work as documented in the AI SDK docs. These are the parameters with model-specific behavior.

Parameter	Type	Required	Description
`model`	`string`	Yes	Model ID in the form `creator/model`, e.g. `nvidia/nemotron-3-nano-30b-a3b`. AI Gateway routes the request to an available provider.
`maxOutputTokens`	`number`	No	Hard cap on generated tokens. Nemotron 3 Nano 30B A3B supports up to `262,144` output tokens. Reasoning tokens count toward this limit.
`reasoning`	`'provider-default' \| 'none' \| 'minimal' \| 'low' \| 'medium' \| 'high' \| 'xhigh'`	No	Provider-agnostic reasoning effort, available in AI SDK 7 or later. Maps to the provider’s native reasoning configuration; reasoning settings under `providerOptions` take precedence when both are set. See the Reasoning section below.
`providerOptions`	`Record<string, JSONValue>`	No	AI Gateway routing options under `gateway`, plus any provider-native options under the provider’s own namespace — see the table below.

Input limits

Input	Formats	Sources	Max count	Max size	Limits
Text	—	—	—	—	Prompt and response share the 262K-token context window

Provider options

Set AI Gateway routing options under providerOptions.gateway. For provider-specific options, pass them under the provider’s namespace as documented by the AI SDK.

Learn more in the AI SDK provider docs.

provider-options.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'nvidia/nemotron-3-nano-30b-a3b',
    prompt: 'Why is the sky blue?',
    providerOptions: {
      gateway: {
        only: ['deepinfra'],
      },
    },
  });

  console.log(result.text);
}

main().catch(console.error);

These AI Gateway routing options apply to every model. Provider-specific options pass through under the provider’s own namespace (for example providerOptions.anthropic) exactly as documented by the AI SDK.

Parameter	Type	Required	Description
`providerOptions.gateway.only`	`string[]`	No	Restrict routing to these provider slugs. Requests fail over only within the listed providers.
`providerOptions.gateway.order`	`string[]`	No	Preferred provider order. Listed providers are tried first; unlisted providers remain available as fallbacks.
`providerOptions.gateway.sort`	`'cost' \| 'ttft' \| 'tps'`	No	Rank candidate providers by price, time to first token, or tokens per second instead of the default routing order.
`providerOptions.gateway.zeroDataRetention`	`boolean`	No	Route only to providers with a zero-data-retention policy for this model.

Routing across providers

AI Gateway serves the same model through multiple providers and fails over automatically. order expresses a preference while keeping every provider eligible; only is a hard allowlist — if none of the listed providers are available the request fails instead of falling back.

Options under a provider's own namespace (for example providerOptions.anthropic) are forwarded to that provider with the request. Providers ignore option namespaces that don't apply to them, so it is safe to set provider options alongside gateway routing options.

Reasoning

AI Gateway bridges reasoning across every API format. The AI SDK exposes a provider-agnostic top-level reasoning level (none, minimal, low, medium, high, or xhigh); the Chat Completions and Responses formats take the same effort under reasoning.effort; and the Anthropic Messages format uses a native thinking token budget. Whichever you send, the gateway maps it to the target model’s native configuration, converting between effort levels and token budgets as needed. Reasoning-related settings under providerOptions take full precedence over the top-level reasoning value and are never merged. Reasoning tokens typically count toward your output-token usage, though how they’re reported and billed varies by provider.

Learn more in the AI Gateway reasoning guide.

reasoning.ts

import { generateText } from 'ai';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'nvidia/nemotron-3-nano-30b-a3b',
    prompt: 'Explain the Monty Hall problem step by step.',
    reasoning: 'high',
  });

  console.log(result.text);
}

main().catch(console.error);

Tool calling

Expose tools the model can call. Define each tool’s inputs with a Zod schema.

tool-calling.ts

import { generateText, tool } from 'ai';
import { z } from 'zod';
import 'dotenv/config';

async function main() {
  const result = await generateText({
    model: 'nvidia/nemotron-3-nano-30b-a3b',
    prompt: 'What is the weather in San Francisco?',
    tools: {
      getWeather: tool({
        description: 'Get the current weather for a location',
        inputSchema: z.object({ location: z.string() }),
        execute: async ({ location }) => ({ location, temperatureC: 18 }),
      }),
    },
  });

  console.log(result.text);
}

main().catch(console.error);

Copy link to headingMore models by NVIDIA

Model

Context	Latency	Throughput	Input	Output	Cache	Web Search	Capabilities	Providers	ZDR	No Training	Free Tier	Release Date

nvidia/nemotron-3.5-lightning

262K

0.2 s

521 tps

$0.05/M

$0.15/M

Read$0.01/M

—

08/11/2026

nvidia/nemotron-3-ultra-550b-a55b

0.3 s

186 tps

$0.50/M

$2.40/M

Read$0.12/M

—

06/04/2026

nvidia/nemotron-3-super-120b-a12b

256K

0.2 s

130 tps

$0.15/M

$0.65/M

—

03/11/2026

nvidia/nemotron-nano-12b-v2-vl

131K

0.1 s

45 tps

$0.20/M

$0.60/M

—

10/28/2025

nvidia/nemotron-nano-9b-v2

131K

0.2 s

149 tps

$0.06/M

$0.23/M

—

08/18/2025

Copy link to headingAbout Nemotron 3 Nano 30B A3B

NVIDIA announced Nemotron 3 Nano 30B A3B on December 15, 2025 as the first model in the Nemotron 3 family. The core idea is architectural efficiency at scale. 30B total parameters provide a broad knowledge base, but only 3B activate for any given token. This keeps inference cost and speed in the range of much smaller models.

Three layer types interleave throughout the architecture. Mamba-2 layers handle sequence processing with linear-time complexity. This makes the context window of 262.1K tokens feasible without the quadratic memory growth of pure attention. Transformer attention layers appear at strategic depths to maintain precise associative recall: the ability to pick out a specific fact from a large context. Mixture-of-experts (MoE) routing selects which expert parameters activate for each token, keeping compute proportional to the 3B active count rather than the full 30B.

Weights and recipes are available under the NVIDIA Open Model License. Deployment cookbooks for vLLM, SGLang, and TensorRT-LLM are also provided. Overview and techniques: https://deepinfra.com/nvidia/Nemotron-3-Nano-30B-A3B.

Copy link to headingWhat To Consider When Choosing a Provider

Configuration: With a context window of 262.1K tokens, entire codebases or multi-document evidence sets fit in a single call. Plan context usage carefully. Filling the window is possible, but model the cost and latency implications ahead of time. Compare $0.05 and $0.24.
Zero Data Retention: Zero Data Retention is available for this model. It is offered on a per-provider and model basis. See the documentation for details.
Authentication: AI Gateway authenticates requests using an API key or OIDC token. You do not need to manage provider credentials directly.

Copy link to headingWhen to Use Nemotron 3 Nano 30B A3B

Best for

Concurrent multi-agent systems: Running many lightweight agents where per-agent throughput matters
Long-context tasks: Holding entire codebases, extended session histories, or multi-document sets in one call
Agentic tool-calling workflows: Multi-step pipelines with chained actions

Consider alternatives when

Maximum reasoning depth: Nemotron 3 Super (120B/12B active) handles complex multi-agent planning
Vision-language tasks: Nemotron Nano 12B v2 VL is the multimodal option
Smaller context needs: A 128K context window is sufficient and the 262.1K tokens capacity goes unused
Compact dense reasoning: Nemotron Nano 9B v2 targets a dense model profile

Copy link to headingConclusion

Nemotron 3 Nano 30B A3B delivers the throughput of a small model with the knowledge breadth of a large one. Its hybrid Mamba-Transformer MoE architecture and context of 262.1K tokens suits tasks that require holding large amounts of information in a single pass. Use AI Gateway to route traffic with unified auth.

Copy link to headingFrequently Asked Questions

Why does "30B total, 3B active" matter for inference cost?
You pay for compute proportional to the active parameters, not the total. Nemotron 3 Nano 30B A3B runs at speeds and costs closer to a 3B dense model but draws on 30B parameters of learned knowledge. The MoE routing mechanism selects the relevant subset per token.
How does the Mamba architecture enable the context of 262.1K tokens?
Mamba layers process sequences with linear-time complexity rather than the quadratic scaling of standard attention. That makes it practical to hold 262.1K tokens in context without the memory explosion that would make pure-attention models infeasible at that length.
How does Nemotron 3 Nano 30B A3B differ from Nemotron Nano 9B v2?
They use different architectures. Nemotron 3 Nano 30B A3B is a sparse MoE with 30B total/3B active parameters and a context window of 262.1K tokens. Nemotron Nano 9B v2 is a dense 9B model with a 128K-token context window. Choose Nemotron 3 Nano 30B A3B for throughput across multi-agent systems and Nano 9B v2 as a compact reasoning model.
Where are hosted input and output prices listed?
Current pricing is shown on this page. AI Gateway routes across providers, and rates may vary by provider.

Your use is subject to NVIDIA's Terms & Privacy Policies.

Agent Stack

Core Platform

Tools

Learn

Build

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Nemotron 3 Nano 30B A3B

Copy link to headingPlayground

Copy link to headingProviders

Copy link to headingUptime24 hours

Copy link to headingThroughput24 hours

Copy link to headingLatency24 hours

Getting started

Top-level parameters

Input limits

Provider options

Routing across providers

Reasoning

Tool calling

Copy link to headingMore models by NVIDIA

Copy link to headingAbout Nemotron 3 Nano 30B A3B

Copy link to headingWhat To Consider When Choosing a Provider

Copy link to headingWhen to Use Nemotron 3 Nano 30B A3B

Best for

Consider alternatives when

Copy link to headingConclusion

Copy link to headingFrequently Asked Questions