Nemotron 3.5 Super and Lightning: Open, Efficient Mixture-of-Experts Hybrid Mamba-Transformer Models for Agentic AI

Key points

NVIDIA released Nemotron 3.5 Super, a 120B-parameter, 12B-active multimodal Mixture-of-Experts Hybrid Mamba-Transformer model, and Lightning, a 30B-parameter, 3B-active text model.
Both models went through a 100B-token MTP warm-up, 1 trillion tokens of distillation from a larger Nemotron model, and long-context training, while Super added Indic continued pretraining and agentic software-engineering mid-training.
The agentic mid-training raised DeepSWE pass@1 from 6.4% to 10.4% and SWE-bench Verified under the Codex harness from 48.4% to 61.8% under identical experimental SFT.
The post-trained Super model achieves 51.78% on SWE-bench Pro, surpassing Nemotron 3 Ultra with approximately 4.6× fewer active parameters, and parallel agent execution with self-verification improved Terminal-Bench 2.1 from 62.7% to 71.9%.
NVFP4 quantization achieves 99.62% median accuracy recovery relative to BF16 across 36 benchmarks, and MTP boosting improves speculative-decoding acceptance length by up to 21.8% under probabilistic draft sampling.
On a single GB200 GPU it produces up to 4,856 output tokens/s/GPU while sustaining at least 50 tokens/s/user, and NVFP4 delivers 2.43× the throughput of BF16 under the evaluated interactive agentic workload.

External research summaries. These are not HDATF publications or measured product results.