Nemotron-Labs-Diffusion: A Tri-Mode Language Model Unifying Autoregressive, Diffusion, and Self-Speculation Decoding
Authors: name: Yonggan Fu, name: Lexington Whalen, name: Abhinav Garg, name: Chengyue Wu, name: Maksim Khadkevich, name: Nicolai Oswald, name: Enze Xie, name: Daniel Egert, name: Sharath Turuvekere Sreenivas, name: Shizhe Diao, name: Chenhan Yu, name: Ye Yu, name: Weijia Chen, name: Sajad Norouzi, name: Jingyu Liu, name: Shiyi Lan, name: Ligeng Zhu, name: Jin Wang, name: Jindong Jiang, name: Morteza Mardani, name: Mehran Maghoumi, name: Song Han, name: Ante Jukić, name: Nima Tajbakhsh, name: Jan Kautz, name: Pavlo Molchanov
Organization: NVIDIA

We introduce Nemotron-Labs-Diffusion, a tri-mode language model (LM) that unifies AR, diffusion, and self-speculation decoding within a single architecture. Trained with a joint AR-diffusion objective, Nemotron-Labs-Diffusion can switch modes to sustain high throughput across deployment settings and concurrency levels. Our study shows that (1) AR and diffusion objectives are complementary: diffusion improves lookahead planning, while AR provides left-to-right linguistic priors. (2) In self-speculation mode, diffusion drafts while AR verifies, outperforming multi-token prediction (MTP) methods in both acceptance rate and real-device efficiency. (3) A speed-of-light analysis further demonstrates diffusion's long-term potential, with up to 76.5% more tokens per forward pass than self-speculation under an optimal sampler. Scaling to 3B, 8B, and 14B parameters, our Nemotron-Labs-Diffusion family, including base, instruct, and vision-language models, consistently outperforms state-of-the-art open-source AR and diffusion LMs in both accuracy and speed. For example, Nemotron-Labs-Diffusion-8B decodes 6x more tokens per forward than Qwen3-8B with comparable accuracy, translating to 4x higher throughput on SPEED-Bench with SGLang on a GB200 GPU.