NVIDIA Nemotron 3.5 Lightning 30B A3B NVFP4 DSpark

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

by NVIDIA

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

Released July 20261049K context≈ 786,432 words

A lean, efficient text model that punches above its weight through sparse activation — only 3 billion parameters fire per forward pass despite a 30 billion parameter total, keeping compute costs low while retaining broader knowledge capacity. It handles an unusually large context window of over one million tokens, making it capable of processing very long documents in a single pass. The NVFP4 quantization format reflects a focus on deployment efficiency over raw precision.

Capabilities

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

Multilingual

Moderate

Long Context

NVIDIA Nemotron 3.5 Lightning 30B A3B NVFP4 DSpark

by NVIDIA

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

Released July 20261049K context≈ 786,432 words

A lean, efficient text model that punches above its weight through sparse activation — only 3 billion parameters fire per forward pass despite a 30 billion parameter total, keeping compute costs low while retaining broader knowledge capacity. It handles an unusually large context window of over one million tokens, making it capable of processing very long documents in a single pass. The NVFP4 quantization format reflects a focus on deployment efficiency over raw precision.

Capabilities

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

Multilingual

Moderate

Long Context

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.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.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