英伟达再出手！新型混合架构模型问世，两大创新实现53.6倍吞吐提速

Core Insights - The article introduces Jet-Nemotron, a new hybrid architecture language model developed by researchers from NVIDIA, which achieves state-of-the-art (SOTA) accuracy while significantly improving efficiency compared to existing full-attention models [2][8][9]. Model Performance - Jet-Nemotron-2B outperforms several leading open-source full-attention models, including Qwen3, Qwen2.5, Gemma3, and Llama3.2, while achieving a throughput acceleration of up to 53.6 times on H100 GPUs with a context length of 256K and maximum batch size [2][9]. - In benchmark tests such as MMLU and MMLU-Pro, Jet-Nemotron's accuracy surpasses that of advanced MoE full-attention models, despite those models having larger parameter sizes [2][5]. Innovations and Techniques - Jet-Nemotron is built on two core innovations: Post Neural Architecture Search (PostNAS) and JetBlock, a new linear attention module that significantly enhances performance compared to previous designs like Mamba2 [6][21]. - PostNAS allows for efficient architecture exploration and adaptation on pre-trained Transformer models, reducing the cost and risk associated with developing new language model architectures [12][16]. Efficiency and Accuracy - The architecture of Jet-Nemotron enables immediate improvements in efficiency and accuracy, leading to better service quality and reduced operational costs [17]. - The hardware-aware search conducted by PostNAS identifies architectures that maintain similar throughput while achieving higher accuracy with more parameters [18]. Comparative Results - Jet-Nemotron-2B and Jet-Nemotron-4B demonstrate competitive accuracy against leading efficient language models, with Jet-Nemotron-4B being 21 times faster and Jet-Nemotron-2B being 47 times faster than Qwen3-1.7B-Base [23][24].