Agentic enterprise systems
Build reasoning, planning, coding, tool-calling, and retrieval agents under an organization-controlled deployment.
Independent tool overview
Nemotron 3 at a glance
NVIDIA Nemotron 3 is a family of downloadable models and hosted NIM endpoints for agentic reasoning, coding, tool use, long-context work, multimodal understanding, and retrieval.
Visit the official Nemotron 3 site ↗
- Developer
- NVIDIA
- Core sizes
- Nano, Super, and Ultra
- Largest context
- Up to 1 million tokens
- Deployment
- Download, NIM, or partner endpoint
Overview
What Nemotron 3 is
Nemotron 3 is NVIDIA's open-model family for developers building specialized AI agents and enterprise inference systems. Its core Nano, Super, and Ultra language models use hybrid Mamba-Transformer mixture-of-experts architectures that activate only part of their total parameter count, aiming to balance reasoning quality, long context, and serving efficiency.
The family has expanded beyond the original three sizes. Nemotron 3 Nano Omni adds image, video, audio, and text understanding, while Nemotron 3 Embed supports multilingual semantic search and retrieval. Weights, model cards, training resources, and deployment instructions are available through NVIDIA and Hugging Face, with free prototype endpoints and production options through NIM, partner endpoints, or self-hosted infrastructure.
Use cases
Who Nemotron 3 is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
High-throughput inference
Use mixture-of-experts models that activate a smaller parameter subset per token than their total model size.
Multimodal document and media analysis
Use Nano Omni for text output from images, video, audio, documents, OCR, and graphical interfaces.
Capabilities
Core Nemotron 3 features
Three core reasoning sizes
Nano has 30B total and about 3.5B active parameters, Super has 120B total and 12B active, and Ultra has 550B total and 55B active.
Configurable reasoning
The chat template can enable or disable thinking mode to trade additional reasoning work for speed and cost.
Long context
Super and Ultra support context windows up to one million tokens for large repositories, documents, and multi-step agent histories.
Tool use and coding
The core models are trained for instruction following, code, planning, retrieval, and tool-calling workflows.
Nano Omni
A roughly 31B-total, 3B-active model that understands text, images, audio, and video and returns text with up to 256K context.
Open deployment paths
Provides downloadable checkpoints plus integrations for vLLM, SGLang, TensorRT-LLM, Ollama, llama.cpp, NVIDIA NIM, and partner services.
Process
How the Nemotron 3 workflow works
Step 1
Match the model to the workload
Choose Nano for efficiency, Super for stronger long-context agents, Ultra for demanding reasoning, Omni for multimodal input, or Embed for retrieval.
Step 2
Check license and hardware
Review the exact model card, governing license, supported precision, memory requirement, and deployment geography before committing.
Step 3
Prototype with an endpoint
Test prompts, tool schemas, context behavior, and output quality through NVIDIA's free trial endpoint or another provider.
Step 4
Evaluate on private tasks
Measure accuracy, latency, throughput, safety, and total cost against the organization's real documents and agent workflows.
Step 5
Deploy and monitor
Select NIM, a partner endpoint, or self-hosted inference, then add application-level permissions, observability, evaluation, and fallback controls.
Cost
Nemotron 3 pricing and free plan
Nemotron 3 model weights can be downloaded under the license attached to each checkpoint, and NVIDIA offers free prototype endpoints. Production cost depends on GPU infrastructure, NVIDIA NIM licensing or entitlement, cloud and partner endpoint rates, model size, precision, context length, and traffic.
Downloadable weights
No model download fee
Run eligible Nemotron 3 checkpoints on infrastructure you operate.
- Model-specific NVIDIA or OpenMDW license
- Compute and operations paid separately
- Several precision variants available
NVIDIA prototype endpoint
Free trial endpoint
Prototype through an OpenAI-compatible hosted API subject to NVIDIA's trial terms and limits.
- API key required
- Not a universal production pricing commitment
- Availability varies by model
Production deployment
Infrastructure or provider pricing varies
Use NVIDIA NIM, partner endpoints, or self-managed GPUs for production inference.
- Hardware requirements rise sharply by model size
- Cloud GPU and endpoint rates vary
- Long context increases memory and compute demand
Pricing checked . Check current pricing at the source ↗
Assessment
Nemotron 3 strengths and limitations
Where it stands out
- Provides multiple efficiency and quality points instead of forcing one model size on every workload
- Publishes model weights, detailed cards, training resources, and deployment integrations
- Supports configurable reasoning, tool use, coding, retrieval, and long-context agent workflows
- Fits NVIDIA's optimized serving stack while remaining available to several open inference frameworks
What to consider
- The name covers several materially different models, context limits, modalities, licenses, and hardware profiles
- Super and Ultra require substantial GPU capacity; NVIDIA lists eight H100 80GB GPUs as the minimum BF16 configuration for each
- A free prototype endpoint does not make production inference free, and NVIDIA does not publish one universal hosted price for the family
- Benchmarks and vendor evaluations do not replace testing on private data, tool schemas, safety requirements, latency targets, and total cost
Compare
Nemotron 3 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Coding
Meta Llama Models
Choose Meta Llama when ecosystem breadth and broad third-party deployment support are the main priorities.
Explore Meta Llama Models →Business Operations
DeepSeek
Choose DeepSeek for another family of downloadable reasoning and coding models with separate hosted access.
Explore DeepSeek →Consumer
Qwen3.5 Small
Choose Qwen3.5 Small when compact open models and more modest local hardware targets are more important than NVIDIA-stack optimization.
Explore Qwen3.5 Small →Questions
Nemotron 3 FAQs
Is Nemotron 3 one model?
No. It is a family that includes Nano, Super, Ultra, Nano Omni, embedding models, multiple precisions, and base or post-trained variants.
What are the main Nemotron 3 sizes?
Nano is 30B total with about 3.5B active parameters, Super is 120B total with 12B active, and Ultra is 550B total with 55B active.
Can Nemotron 3 process images or video?
The core Nano, Super, and Ultra chat models are text models. Nemotron 3 Nano Omni accepts video, audio, image, and text inputs and returns text.
Is Nemotron 3 free?
Weights can be downloaded without a per-model purchase fee and NVIDIA offers free trial endpoints, but self-hosting, production endpoints, NIM, cloud GPUs, and operations can create substantial costs.
Can Nemotron 3 be used commercially?
NVIDIA model cards mark major Nemotron 3 checkpoints as ready for commercial use, but the exact governing license differs by checkpoint and must be reviewed before deployment.
Bottom line
Our Nemotron 3 verdict
Nemotron 3 is particularly attractive to teams that want inspectable, deployable agent models and already operate NVIDIA infrastructure. Nano is the practical starting point, while Super and Ultra should be selected only after workload-specific testing justifies their much larger hardware footprint; teams should treat each checkpoint as a separate technical and licensing decision.
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