NVIDIA Nemotron 3.5 Lightning 30B A3B NVFP4

Name: NVIDIA Nemotron 3.5 Lightning 30B A3B NVFP4
Author: NVIDIA

by NVIDIA

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

Released August 20261049K context≈ 786,432 words30B params

A lean, efficient text model that punches above its weight through sparse activation — only 3 billion parameters fire per forward pass despite the 30B total parameter count. This mixture-of-experts design means faster inference and lower compute costs without sacrificing the breadth of a larger model. It handles long contexts exceptionally well, with a context window stretching to over a million tokens.

Capabilities

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

Long Context

Exceptional

Instruction Following

NVIDIA Nemotron 3.5 Lightning 30B A3B NVFP4

by NVIDIA

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

Released August 20261049K context≈ 786,432 words30B params

A lean, efficient text model that punches above its weight through sparse activation — only 3 billion parameters fire per forward pass despite the 30B total parameter count. This mixture-of-experts design means faster inference and lower compute costs without sacrificing the breadth of a larger model. It handles long contexts exceptionally well, with a context window stretching to over a million tokens.

Capabilities

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

Long Context

Exceptional

Instruction Following

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.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.Parameter CountThe total number of adjustable weights in a model; more parameters generally mean more capacity to learn, but also require more computing power.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.Sparse ActivationA technique where only a subset of a model's parameters are used for each input, reducing computational cost while maintaining performance.Text ModelA language model that processes and generates only text, without support for images, audio, or other media types.TokensThe basic units of text that a language model processes, typically representing words or word fragments.

Capabilities

Capabilities

Use Case Fit

Glossary