Sponsored by Byond Boundrys Consulting - Empowering Ideas, Delivering Results
NVIDIA Advanced

NVIDIA Nemotron 3 Super 120B A12B

NVIDIA Nemotron 3 Super 120B A12B is an open-weight 120B (12B active) reasoning model for agents, RAG and tool use, with up to 1M context and downloadable weights.

Reasoning ModelsText Freemium
In plain English

What is this model and why does it matter?

NVIDIA Nemotron 3 Super 120B A12B is a 120B-total, 12B-active open-weight reasoning model with up to 1M context, optimized for agents, RAG, tool use and high-volume workloads.

AgentsRAGIT automationLong-context reasoningPrivate deployments
Model overview

NVIDIA Nemotron 3 Super 120B A12B: features, use cases and important details

NVIDIA Nemotron 3 Super 120B A12B is a current AI model verified from first-party NVIDIA sources.

NVIDIA Nemotron 3 Super 120B A12B verified specifications

NVIDIA Nemotron 3 Super 120B A12B is a 120B-total, 12B-active open-weight reasoning model with up to 1M context, optimized for agents, RAG, tool use and high-volume workloads. Its verified context or usage limit is Up to 1000000 tokens, with Deployment configurable; official examples use up to 32000 generated tokens maximum output.

NVIDIA Nemotron 3 Super 120B A12B pricing and access

NVIDIA provides a free prototype NIM endpoint and downloadable weights. Production partner/self-hosted cost depends on the selected infrastructure rather than a single published token price.

NVIDIA Nemotron 3 Super 120B A12B best uses

Agents, RAG, IT automation, Long-context reasoning, Private deployments.

NVIDIA Nemotron 3 Super 120B A12B limitations

Default self-host configurations may use shorter context, Large GPU requirements for full-scale deployment, Requires use-case testing.

Get started

How to use this model

  1. Generate an NVIDIA API key for the prototype or download the weights.
  2. Call nvidia/nemotron-3-super-120b-a12b.
  3. Enable or disable reasoning through the chat template.
  4. Connect tool parsing where required.
  5. Benchmark 256K versus 1M deployment memory needs.
Copy and try

Example prompts

  • Automate triage for these IT tickets.
  • Reason across this long enterprise knowledge base.
  • Use tools to solve this multi-step operational task.
Capabilities

What it can do

  • 120B total / 12B active
  • 1M context
  • Configurable reasoning
  • Tool use
  • Structured outputs
  • Downloadable weights
Best for

Practical use cases

  • Enterprise agents
  • RAG
  • IT automation
  • Private assistants
  • Long-context analysis
Pricing

What does it cost?

NVIDIA provides a free prototype NIM endpoint and downloadable weights. Production partner/self-hosted cost depends on the selected infrastructure rather than a single published token price.

InputFree prototype; production infrastructure dependent
OutputFree prototype; production infrastructure dependent
Simple summaryA free prototype endpoint is available; production cost depends on partner or self-hosted GPU infrastructure.

What stands out

  • 65M-class recent NIM usage signal
  • 1M context
  • Downloadable weights
  • Commercial use
  • Free prototype endpoint

Things to consider

  • Large self-hosting footprint
  • Custom NVIDIA license rather than standard OSI license
  • Text only
Limitations

Important restrictions and trade-offs

  • Default self-host configurations may use shorter context
  • Large GPU requirements for full-scale deployment
  • Requires use-case testing
SimplifyAITools verdict

Our editorial take

One of NVIDIA’s most-used current Nemotron models and a strong high-demand addition for open-weight enterprise agent searches.

References

Primary sources

  1. Open source 1 ↗
  2. Open source 2 ↗