Nemotron 3 Nano Omni 30B A3B Reasoning NVFP4

Name: Nemotron 3 Nano Omni 30B A3B Reasoning NVFP4
Author: NVIDIA

by NVIDIA

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

Released April 2026context N/A

A compact reasoning-focused model from Nvidia that punches above its weight through sparse activation — only 3 billion parameters fire per forward pass despite a 30 billion parameter total. It runs in NVFP4 precision, a Nvidia-specific quantization format that trades some numerical fidelity for significant memory and speed gains. Expect deliberate, step-by-step reasoning behavior, though its reliance on Nvidia-specific formats limits portability to other hardware ecosystems.

Capabilities

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

Reasoning & Logic

Strong

Factual Knowledge

Nemotron 3 Nano Omni 30B A3B Reasoning NVFP4

by NVIDIA

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

Released April 2026context N/A

A compact reasoning-focused model from Nvidia that punches above its weight through sparse activation — only 3 billion parameters fire per forward pass despite a 30 billion parameter total. It runs in NVFP4 precision, a Nvidia-specific quantization format that trades some numerical fidelity for significant memory and speed gains. Expect deliberate, step-by-step reasoning behavior, though its reliance on Nvidia-specific formats limits portability to other hardware ecosystems.

Capabilities

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

Reasoning & Logic

Strong

Factual Knowledge

Glossary

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.NVFP4 PrecisionA low-precision numerical format that uses 4 bits per weight, developed by NVIDIA to compress models for efficient inference on consumer hardware.NVFP4 PrecisionA low-precision numerical format optimized by NVIDIA that uses fewer bits per number than standard formats, enabling efficient inference on NVIDIA GPUs while maintaining reasonable accuracy.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.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.Reasoning-FocusedA model specifically trained to work through multi-step logical problems methodically rather than generating quick responses.Sparse ActivationA technique where only a subset of a model's parameters are used for each input, reducing computational cost while maintaining performance.Step-by-Step ReasoningAn approach where the model explicitly works through intermediate reasoning steps before arriving at a final answer, rather than jumping directly to conclusions.

Capabilities

Capabilities

Use Case Fit

Glossary