Nemotron 3 Nano Omni 30B A3B Reasoning BF16

Name: Nemotron 3 Nano Omni 30B A3B Reasoning BF16
Author: NVIDIA

by NVIDIA

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A compact mixture-of-experts reasoning model from NVIDIA that activates only 3 billion parameters out of 30 billion total, keeping inference costs low while tackling multi-step logical problems. Its BF16 precision and safetensors format make it straightforward to deploy on consumer hardware. The trade-off is that sparse activation can occasionally miss nuance that denser models catch.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Moderate

Coding

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Nemotron 3 Nano Omni 30B A3B Reasoning BF16

by NVIDIA

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released April 2026context N/A

A compact mixture-of-experts reasoning model from NVIDIA that activates only 3 billion parameters out of 30 billion total, keeping inference costs low while tackling multi-step logical problems. Its BF16 precision and safetensors format make it straightforward to deploy on consumer hardware. The trade-off is that sparse activation can occasionally miss nuance that denser models catch.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Reasoning & Logic

Strong

Factual Knowledge

Moderate

Coding

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

BF16A 16-bit floating-point format that balances precision and memory efficiency, commonly used for training and deploying large language models.BF16 PrecisionA 16-bit numerical format that balances memory efficiency with numerical stability, using fewer bits than standard 32-bit floats while maintaining training and inference quality.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning ModelA model trained to show explicit step-by-step reasoning and problem-solving logic before producing final answers, rather than jumping directly to conclusions.SafetensorsA safe, fast file format for storing model weights, designed to prevent code execution vulnerabilities.Safetensors FormatA secure and efficient file format for storing model weights that prioritizes safety and speed when loading models.Sparse ActivationA technique where only a subset of a model's parameters are used for each input, reducing computational cost while maintaining performance.