NVIDIA AI unleashes Nemotron Nano 2 AI model: a family of production-readable enterprise AI models 6 times faster than similar-sized models

NVIDIA unveiled the Nemotron Nano 2 family, introducing a series of hybrid Mamba-Transformer Big Speech models (LLMS) that not only improve the latest inference accuracy, but also provide higher recommended throughput than similarly sized models. Since NVIDIA provides the community with most training corpus and recipes, this version has unprecedented transparency in data and methodology. Crucially, these models maintain a huge 128k token context capability on a single mid-range GPU, which greatly reduces barriers to long-term cultural reasoning and real-world deployment.

Key Highlights

• 6× throughput and similar size model: The Nemotron Nano 2 model offers up to 6.3x token generation speeds for models in heavier scenarios like the Qwen3-8b without sacrificing accuracy.
• Excellent accuracy for inference, coding and multilingual tasks: Benchmarks show peers in PAR or better results with competitive open models, especially over math, code, tool usage and long-form tasks.
• 128K context length on a single GPU: Efficient pruning and hybrid architecture enables 128,000 token inferences to a single NVIDIA A10G GPU (22GIB).
• Open data and weights: Most training and post-training datasets, including code, math, multilingual, synthetic SFT and inference data, release loose permissions on the embrace surface.

Hybrid architecture: Mamba meets Transformers

Nemotron Nano 2 is built on a hybrid Mamba conversion skeleton inspired by Nemotron-H Architecture. Most traditional self-initiating layers are replaced by the effective Mamba-2 layers, with only about 8% using self-attention. The architecture is carefully crafted:

• Model details: The 9B parameter model has 56 layers (in pre-trained 62 layers), a hidden size of 4480, and has grouped questions and MAMBA-2 state space layers that promote scalability and long sequence retention.
• Mamba-2 Innovation: These state space layers have recently been popularized as high-throughput sequence models, intertwined with sparse self-cautions (to maintain long-distance dependencies) and large feed forward networks.

This structure allows long-generation effects of reasoning tasks that require “thread of thinking” (based on long, internal intrinsic inputs) to allow traditional transformer-based architectures to slow down or drain memory.

Training recipe: a lot of data diversity, open source

The Nemotron Nano 2 model is trained using a wide range of high-quality corpus and distilled from the 12B parameter teacher model. Nvidia’s unprecedented data transparency is a standout:

• The 20T tokens are pre-trained: Data sources include curated and synthetic corpus for the fields of networking, mathematics, code, multilingual, academic and STEM.
• The main data sets published:
  • Nemotron-CC-V2: Multilingual web crawling (15 languages), synthetic question and answer redesign, deduplication.
  • Nemotron-cc-Math: 133b’s mathematical content token, standardized to latex, exceeds 52b’s “highest quality” subset.
  • Nemotron-ropratraining Code: Curated and quality filtered GitHub source code; strict decontamination and deduplication.
  • nemotron-pretrating-sft: Synthesis of cross stemming, reasoning and general domains, guides tracking datasets.
• Post-training data: Includes over 80b of supervised fine-tuning (SFT), RLHF, tool name and multilingual dataset tokens, most of which are open source, direct repeatability.

Alignment, distillation and compression: Unlocking cost-effectiveness, long cultural reasoning

NVIDIA’s model compression process is built on the “Minitron” and Mamba trimming framework:

• Knowledge distillation Teachers from 12B reduce the model to 9b parameters and carefully trim the layers, FFN size and embed width.
• Multi-stage SFT and RL: Including tool call optimization (BFCL V3), guidance compliance (IFEVAL), DPO and GRPO enhancement measures, and “think budget” control (support for controllable inference budgets when reasoning).
• NAS targeting memory: Through architecture search, the trimmed model is designed so that both the model and key-value cache are suitable and still behaved, and in 128K context length in A10G GPU memory.

Results: In case of large input/output tokens, the inference speed is at most 6×× faster than the open competitor without compromising task accuracy.

Benchmark: Excellent reasoning and multilingual skills

In head-to-head evaluation, the Nemotron Nano 2 model is excellent:

• Throughput of 8K input/16K output (token/s/gpu):
  • Nemotron-Nano-9B-V2: QWEN3-8B can be available in the inference trajectory.
  • Keep up to 128k-contept on mid-segment GPU, batch size = 1 – impractical and unrealistic.

in conclusion

NVIDIA’s Nemotron Nano 2 version is an important moment in open LLM research: it redefines the possibility of a single cost-effective GPU (in terms of speed and context capacity) while improving standards of data transparency and repeatability. Its hybrid architecture, throughput and high-quality open datasets will accelerate innovation across the entire AI ecosystem.

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Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.