Nvidia Nemotron 3.5 Super and Lightning

We introduce NVIDIA Nemotron 3.5 Super and NVIDIA Nemotron 3.5 Lightning, open and efficient Mixture-of-Experts Hybrid Mamba-Transformer models designed for reasoning, long-context understanding, and agentic workloads.

Nemotron 3.5 Super is a 120-billion-parameter, 12-billion-active multimodal model, while Nemotron 3.5 Lightning is a smaller 30-billion-parameter, 3-billion-active text model. Both are designed for efficient deployment and combine large-scale distillation, long-context training, native speculative decoding, and efficiency-focused quantization. Super further supports reasoning across text, images, video, and interactive tool environments.

Both models begin with a dedicated 100-billion-token MTP warm-up, followed by 1 trillion tokens of distillation from the next-larger Nemotron 3 model. Nemotron 3.5 Super is distilled from Nemotron 3 Ultra Base, while Lightning is distilled from Nemotron 3 Super Base. Following distillation, Lightning undergoes a 21-billion-token long-context phase that extends it to a 1-million-token context length. Super proceeds through the additional continued-pretraining and mid-training stages below.

Nemotron 3.5 Super then undergoes additional training targeted at multilingual, long-context, and agentic use cases:

Super’s post-training pipeline combines supervised fine-tuning, Reinforcement Learning from Verifiable Rewards (RLVR), Reinforcement Learning from Human Feedback (RLHF), specialized teacher training, and Multi-Teacher On-Policy Distillation (MOPD). This process transforms the text-only base model into a multimodal agent while consolidating capabilities from reasoning, agentic, alignment, video, and vision-tool-use teachers. Lightning follows a lighter text-only pipeline using supervised fine-tuning and RLVR.

Nemotron 3.5 is optimized for interactive, long-context agentic workloads. Hybrid-state caching, prefix reuse, Mamba checkpointing, GPU-kernel selection, and speculative decoding are jointly tuned to sustain responsive per-user generation rates at high aggregate throughput.

Post-training MTP boosting improves native speculative decoding, while standalone parallel drafters target low-concurrency, memory-bound deployments:

Nemotron 3.5 Super is validated across NVIDIA A100, H100, B200, GB200, and GB300 platforms, as well as DGX Spark, DGX Station, and RTX-class GPUs. Its quantization and speculative-decoding configurations can be tuned to the target platform and workload.

We are releasing base, post-trained BF16, and NVFP4-quantized checkpoints, together with speculative drafters, datasets, model cards, cookbooks, evaluation recipes, and training recipes.

The Nemotron 3.5 Super and Lightning Technical Report provides full details on pretraining, post-training, quantization, inference optimization, evaluation methodology, and benchmark results.