To: sandeep who wrote (94650 ) 9/1/2025 4:03:08 PM From: Sun Tzu 2 RecommendationsRecommended By
Respond to Is this why nVidia is publishing research on what-if we could speed up AI models by 50x? Because they are not interested in more efficient AI modesl?arxiv.org Jet-Nemotron: Efficient Language Model with Post Neural Architecture Search Yuxian Gu , Qinghao Hu , Shang Yang , Haocheng Xi , Junyu Chen , Song Han , Han Cai Their latest research asks: What if we could make AI models 50x faster without making them worse? And their answer might change how we think about building efficient language models. Here's the problem they solved: Today's most capable AI models use something called "full attention" - imagine every word in a conversation needing to check in with every other word, every single time. It works well, but it's like having everyone in a stadium talk to everyone else individually. The computational cost explodes as conversations get longer. Researchers have tried replacing this with more efficient "linear attention" mechanisms (where the communication scales linearly rather than quadratically), but these models typically perform much worse at scale. It's been a painful trade-off: speed or performance, pick one. Instead of building new models from scratch, NVIDIA started with existing high-performing models and surgically modified them. They froze the parts that already worked well (the parts that store knowledge) and systematically explored which attention layers actually matter for different tasks. From previous research, we already know that not all attention layers are created equal. For general knowledge tasks, only 2 out of 28 layers were critical. For information retrieval, it was a different set of 2-3 layers. This insight let them keep full attention only where absolutely necessary and replace the rest with their new "JetBlock" - a linear attention mechanism enhanced with dynamic convolution that adapts to the input. They aren't the first to do this, this has been a theme for quite a while. It's about the efficiency they did it with: Jet-Nemotron-2B matches or beats models like Qwen3 and Gemma3 across benchmarks while being 47x faster at generation. At longer contexts (256K tokens), the speedup reaches 53.6x. They even outperform recent MoE models with 7x more parameters. How are these numbers even possible? By utilizing what they call "PostNAS": NAS stands Neural Architecture Search - automatically finding the best architecture for your neural network - a process that usually takes a lot of time and/or compute, often more than is feasible. But since they are starting with "already proven" pre-trained models (remember?), they reduce the exploration cost from hundreds of millions of dollars to just thousands in compute. Not only is this clever, it also makes architectural innovations much more accessible, allowing smaller teams to compete with tech giants in designing efficient AI systems.
