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 at once despite the 30B total parameter count, keeping inference costs low. The NVFP4 quantization further trims memory footprint, making it practical to run on constrained hardware. It handles an unusually large context window of over one million tokens, though its text-only focus keeps its scope narrow.

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 at once despite the 30B total parameter count, keeping inference costs low. The NVFP4 quantization further trims memory footprint, making it practical to run on constrained hardware. It handles an unusually large context window of over one million tokens, though its text-only focus keeps its scope narrow.

Glossary

Context WindowThe maximum number of tokens a model can process in a single conversation or prompt.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.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.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.

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Glossary