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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.

Reasoning
index.ts
import { streamText } from 'ai'


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