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A recent survey reveals that many academic researchers face significant challenges in accessing the computing power necessary for artificial intelligence research. The study highlights that a majority of academics are dissatisfied with their computing resources, particularly in obtaining advanced graphics processing units (GPUs) like NVIDIA's H100. This disparity in access between academia and industry hampers the development of large language models and other AI projects.
The findings indicate that while industry giants can afford thousands of GPUs, academic institutions often have only a few, leading to long wait times for access. This situation is exacerbated by global disparities, with some regions, like the Middle East, facing even greater challenges in securing these essential resources. The research emphasizes the need for a competitive academic environment to foster innovation and technological growth in AI.
• Academics report dissatisfaction with access to AI computing resources.
• Only 10% of surveyed academics have access to NVIDIA's H100 GPUs.
GPUs are essential for training AI models, but access is limited for academics.
LLMs require substantial computing power for pre-training, which many academics lack.
AI research is hindered by the lack of access to advanced computing resources.
NVIDIA produces the H100 GPUs, which are critical for AI research and development.
EleutherAI is a non-profit AI research institute advocating for better access to computing resources.
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