斯坦福人均≈0.1张GPU，学术界算力遭“屠杀”，LeCun急了

Core Insights - The academic sector is facing a severe GPU shortage, with many top universities having insufficient resources for AI research compared to industry giants [1][12][17] - The disparity in computational resources between academia and industry is leading to a talent drain, as researchers prefer to work in industry due to better access to GPUs [12][16][20] Group 1: GPU Availability in Academia - Top universities like Princeton and Stanford have an average of 0.8 and 0.14 GPUs per researcher, respectively, which is far below the required amount for substantial AI research [4][3] - Major tech companies have access to tens of thousands of GPUs, with Microsoft's Fairwater Atlanta data center capable of running 23 times the training of GPT-4 in a month [7][12] - Some universities, like the University of Texas at Austin, are investing heavily in AI infrastructure, acquiring over 5,000 NVIDIA GPUs to enhance their research capabilities [27][28] Group 2: Impact on Research and Education - The GPU shortage is reshaping how computer science and engineering are taught, with universities increasingly incorporating GPU-related courses into their programs [15][16] - The lack of resources is making it difficult for academic researchers to conduct experiments, as they often have to queue for GPU access and deal with limited operational hours [21][20] - Some universities are attempting to mitigate the GPU shortage by establishing dedicated AI facilities, such as Cal Poly's AI factory equipped with NVIDIA DGX systems [30][33] Group 3: Talent Migration - The widening gap in computational resources is causing academic researchers to reconsider their career paths, with many opting for industry positions where resources are more abundant [16][12] - The trend is evident in discussions among graduate students, who express frustration over the lack of access to high-performance GPUs for their projects [35][36]