NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid
  Mamba-Transformer Reasoning Model
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model
designed to increase throughput for reasoning workloads while achieving
state-of-the-art accuracy compared to similarly-sized models.
Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the
majority of the self-attention layers in the common Transformer architecture
are replaced with Mamba-2 layers, to achieve improved inference speed when
generating the long thinking traces needed for reasoning. We create
Nemotron-Nano-9B-v2 by first pre-training a 12-billion-parameter model
(Nemotron-Nano-12B-v2-Base) on 20 trillion tokens using an FP8 training recipe.
After aligning Nemotron-Nano-12B-v2-Base, we employ the Minitron strategy to
compress and distill the model with the goal of enabling inference on up to
128k tokens on a single NVIDIA A10G GPU (22GiB of memory, bfloat16 precision).
Compared to existing similarly-sized models (e.g., Qwen3-8B), we show that
Nemotron-Nano-9B-v2 achieves on-par or better accuracy on reasoning benchmarks
while achieving up to 6x higher inference throughput in reasoning settings like
8k input and 16k output tokens. We are releasing Nemotron-Nano-9B-v2,
Nemotron-Nano12B-v2-Base, and Nemotron-Nano-9B-v2-Base checkpoints along with
the majority of our pre- and post-training datasets on Hugging Face.
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NVIDIA Nemotron Nano 2: An Accurate and Efficient Hybrid
  Mamba-Transformer Reasoning Model
We introduce Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer language model
designed to increase throughput for reasoning workloads while achieving
state-of-the-art accuracy compared to similarly-sized models.
Nemotron-Nano-9B-v2 builds on the Nemotron-H architecture, in which the
majority of the self-attention layers in the common Transformer architecture
are replaced with Mamba-2 layers, to achieve improved inference speed when
generating the long thinking traces needed for reasoning. We create
Nemotron-Nano-9B-v2 by first pre-training a 12-billion-parameter model
(Nemotron-Nano-12B-v2-Base) on 20 trillion tokens using an FP8 training recipe.
After aligning Nemotron-Nano-12B-v2-Base, we employ the Minitron strategy to
compress and distill the model with the goal of enabling inference on up to
128k tokens on a single NVIDIA A10G GPU (22GiB of memory, bfloat16 precision).
Compared to existing similarly-sized models (e.g., Qwen3-8B), we show that
Nemotron-Nano-9B-v2 achieves on-par or better accuracy on reasoning benchmarks
while achieving up to 6x higher inference throughput in reasoning settings like
8k input and 16k output tokens. We are releasing Nemotron-Nano-9B-v2,
Nemotron-Nano12B-v2-Base, and Nemotron-Nano-9B-v2-Base checkpoints along with
the majority of our pre- and post-training datasets on Hugging Face.
Nemotron-Nano-9B-v2, a hybrid Mamba-Transformer model, enhances reasoning workload throughput and accuracy by replacing self-attention layers with Mamba-2 layers and using the Minitron strategy for compression.
Mamba-Transformer self-attention layers Mamba-2 layers Minitron strategy inference throughput reasoning benchmarks