Nvidia(US:NVDA) 3 6 Ke·2025-08-27 02:03Core Insights - Nvidia has launched a new series of small models called Jet-Nemotron, developed by an all-Chinese team, featuring innovations such as Post Neural Architecture Search (PostNAS) and a new linear attention module called JetBlock [1][2][8] - Jet-Nemotron models (2B and 4B) outperform leading open-source models like Qwen3, Gemma3, and Llama3.2 in various dimensions including math, code, commonsense, retrieval, and long context accuracy [2][20] - The inference throughput on H100 GPUs has been significantly enhanced, achieving up to a 53.6 times increase [4][20] Model Performance - Jet-Nemotron-2B and Jet-Nemotron-4B demonstrate superior performance in benchmark tests, with Jet-Nemotron-4B achieving a 65.2% accuracy in MMLU, compared to Qwen3's 60.3% [21] - In long context scenarios, Jet-Nemotron shows a dramatic throughput increase, reaching up to 50 times improvement over Qwen3-1.7B [5][20] - The models also exhibit faster speeds, with Jet-Nemotron-2B being 21 times faster and Jet-Nemotron-4B 47 times faster than Qwen3-1.7B-Base [20] Innovations - PostNAS allows for efficient architecture exploration and adaptation based on pre-trained Transformer models, significantly reducing the cost and risk of developing new language model architectures [9][10][14] - JetBlock, a new linear attention module, combines dynamic convolution with hardware-aware architecture search, leading to substantial accuracy improvements while maintaining similar training and inference throughput as previous designs [18][20] Technical Specifications - Jet-Nemotron models have been optimized for various parameters, including cache size and throughput, with configurations achieving a maximum throughput of 2,885 tokens per second [21] - The models utilize a flexible design for attention blocks, allowing for improved performance in long context and complex reasoning tasks [16][18]