NVIDIA-Nemotron-3.5-Lightning-DSpark

Description:

The NVIDIA Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark model is the DSpark speculative decoding checkpoint for NVIDIA's Nemotron-3.5-Lightning-30B-A3B model family, which is a hybrid LatentMoE language model designed for reasoning, chat, and agentic workflows. For more information, please check BF16, NVFP4. The NVIDIA Nemotron-3.5-Lightning-30B-A3B-DSpark-NVFP4 model is intended for DSpark speculative decoding deployments tuned for DGX Spark and low-concurrency data centre workflows.

This model is ready for commercial or non-commercial use.

License/Terms of Use:

GOVERNING DOWNLOAD TERMS: Use of this model is governed by the OpenMDW-1.1 model license.

Deployment Geography:

Global

Use Case:

Developers deploying Nemotron-3.5-Lightning-30B-A3B for reasoning, chat, RAG, and agentic workflows that benefit from lower-latency speculative decoding on DGX Spark and data centre GPUs. This release is intended for DSpark-assisted serving of Nemotron-3.5-Lightning-30B-A3B rather than as a standalone target model checkpoint.

Release Date:

Hugging Face 08/11/2026 via https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark

References

• NVIDIA Model Optimizer: https://github.com/NVIDIA/Model-Optimizer
• NVIDIA Nemotron-3.5-Lightning-30B-A3B-BF16 reasoning model card
• NVIDIA Nemotron-3.5-Lightning-30B-A3B-NVFP4 reasoning model card
• OpenMDW License Agreement, version 1.1
• DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation

Model Architecture:

The DSpark model architecture is as follows:

Architecture Type: Dense GQA (Dense MLP + GQA Attention) Network Architecture: Dense FFN MLP, and GQA Attention layers; DSpark speculative decoding attention uses causal grouped-query attention (GQA) with a sliding window of size 1024 on all layers, and per-head attention sink bias. Number of Model Parameters: 967M total parameters, of which 615M are non-embedding parameters.

For more information about the underlying model's architecture, please see this Nemotron-3.5-Lightning-30B-A3B-BF16, Nemotron-3.5-Lightning-30B-A3B-NVFP4.

Input:

Input Type(s): Text Input Format(s): String Input Parameters: One-Dimensional (1D): Sequences Other Properties Related to Input: Maximum context length up to 1M tokens. Supported languages include English, Spanish, French, German, Italian, and Japanese.

Output:

Output Type(s): Text Output Format: String Output Parameters: One-Dimensional (1D): Sequences Other Properties Related to Output: Outputs may include natural-language responses, reasoning traces, tool-use content, and structured outputs depending on chat-template configuration and application-level tooling.

Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.

Software Integration:

Supported Runtime Engine(s):

• vLLM

Supported Hardware Microarchitecture Compatibility:

• NVIDIA Blackwell - including DGX Spark (GB10)
• NVIDIA Hopper

Preferred Operating System(s):

• Linux

The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.

Model Version(s):

The model is a Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark speculative decoding version quantized with Model Optimizer 0.45.0 version.

Training and Evaluation Datasets:

Training Dataset:

For more information about the underlying Nemotron-3.5-Lightning-30B-A3B model, please visit the model card - Nemotron-3.5-Lightning-30B-A3B-NVFP4, Nemotron-3.5-Lightning-30B-A3B-BF16.

Link: Nemotron-Post-Training-Dataset-v2, and Nemotron-Post-Training-Dataset-v3 only prompts from the datasets were used for data synthesis, (the original responses from GPT were not used), which is then used to train the DSpark modules. Data Modality: Text Text Training Data Size: 66 Billion Tokens, repeated for 2 epochs Data Collection Method by dataset: Hybrid: Automated, manually-collected, Synthetic Labeling Method by dataset: Hybrid: Automated, manually-labelled, Synthetic Properties: The Nemotron-3.5-Lightning-30B-A3B-NVFP4-DSpark model was trained exclusively on a post-training corpus includes synthetic data for reasoning, code, science, tool use, instruction following, structured outputs, and multilingual tasks. Dataset disclosures are published in the nvidia/nemotron-post-training-dataset-v2 and nvidia/nemotron-post-training-v3 Hugging Face collections.

Evaluation Dataset:

The model is ev