NVIDIA-Nemotron-3.5-Super-VL-LiLiCorr

Description:

The NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4-LiLiCorr model is the LiLiCorr speculative decoding checkpoint for NVIDIA's Nemotron-3.5-Super-VL-120B-A12B model family, which is a hybrid LatentMoE vision-language model designed for reasoning, chat, visual understanding, and agentic workflows. For details about the target model, see the BF16 and NVFP4 model cards. The NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4-LiLiCorr model is intended for LiLiCorr speculative decoding deployments tuned for DGX Spark and low-concurrency data center workflows.

This model was built through our ModelOpt library which is a library of model optimization techniques including quantization, pruning, Neural Architecture Search (NAS), distillation, speculative decoding and sparsity to accelerate models.

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-Super-VL-120B-A12B for reasoning, chat, RAG, and agentic workflows that benefit from lower-latency speculative decoding on DGX Spark and data center GPUs. Load this draft checkpoint alongside the Nemotron-3.5-Super-VL-120B-A12B target model to enable LiLiCorr-assisted serving.

Release Date:

Hugging Face - 10/15/2026 via https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Super-VL-120B-A12B-NVFP4-LiLiCorr

References

• NVIDIA Model Optimizer: https://github.com/NVIDIA/Model-Optimizer
• NVIDIA Nemotron-3.5-Super-VL-120B-A12B-BF16 reasoning model card
• NVIDIA Nemotron-3.5-Super-VL-120B-A12B-NVFP4 reasoning model card
• LiLiCorr: Lightweight Likelihood Correlation of Parallel Drafts for Speculative Decoding

Model Architecture:

The LiLiCorr model architecture is as follows:

Architecture Type: Dense GQA (Dense MLP + GQA Attention) Network Architecture: Dense FFN MLP, and GQA Attention layers; the LiLiCorr drafter's attention uses bidirectional grouped-query attention (GQA), with 32 query heads, 8 key-value heads, within the draft block and sliding-window attention over the most recent 8,192 context positions on all layers, with a grouped dynamic convolution around each attention and MLP sublayer. A separate two-layer LiLiCorr head correlates the drafter's top-8 candidates at each draft slot. Number of Model Parameters: 1.13B trained parameters (37M in the LiLiCorr head), plus a 0.54B NVFP4 copy of the target’s LM head.

For details about the target model's architecture, see the Nemotron-3.5-Super-VL-120B-A12B-BF16, Nemotron-3.5-Super-VL-120B-A12B-NVFP4.

Input:

The inputs below describe the target-model serving interface. LiLiCorr drafts tokens using representations from the target model.

Input Type(s): Text Input Format(s): Text: String Input Parameters: Text: One-Dimensional (1D) Other Properties Related to Input: The target model supports up to 1M tokens of context. The serving configuration below sets a 114,688-token limit. Supported languages include English, French, Spanish, Italian, German, Japanese, Hindi, Korean, Brazilian Portuguese, and Chinese.

Output:

Output Type(s): Text Output Format: String Output Parameters: One-Dimensional (1D): Sequences Other Properties Related to Output: Input and generated output share the configured context window. The target model supports JSON output, reasoning output, and tool calling.

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):

• v1.0 - LiLiCorr GA

Training and Evaluation Datasets:

Training Dataset:

For more information about the underlying Nemotron-3.5-Super-VL-120B-A12B model, please visit the model card - Nemotron-3.5-Super-VL-120B-A12B-BF16, Nemotron-3.5-Super-VL-120B-A12B-NVFP4.

Link: Nemotron-Post-Training-Dataset-v2, Nemotron-RL-Super-Training-Blends, Nemotron-Post-Training-v3, Nemotron-Instruction-Following-Chat-v1, and [Ne