Nemotron 3 Nano Omni 30B A3B Reasoning FP8

Name: Nemotron 3 Nano Omni 30B A3B Reasoning FP8
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 per forward pass despite its 30 billion total parameters, keeping inference costs low while tackling structured reasoning tasks. It runs in FP8 precision, trading a small amount of numerical fidelity for significantly faster throughput on compatible hardware.

Capabilities

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

Reasoning & Logic

Strong

Coding

Moderate

Instruction Following

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 FP8

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 per forward pass despite its 30 billion total parameters, keeping inference costs low while tackling structured reasoning tasks. It runs in FP8 precision, trading a small amount of numerical fidelity for significantly faster throughput on compatible hardware.

Capabilities

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

Reasoning & Logic

Strong

Coding

Moderate

Instruction Following

Use Case Fit

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

Glossary

FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.Forward PassA single computation cycle where input data flows through the model's layers to produce an output prediction.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.Reasoning TasksProblems that require a model to think through multiple steps logically to arrive at an answer, rather than just pattern-matching.Structured ReasoningThe ability to follow logical steps and rules systematically to solve problems, often involving breaking down complex tasks into smaller, ordered components.ThroughputThe number of tokens a model can generate per second, measuring its processing speed.