Open-source AI allows developers access to code for models such as Meta's Llama and Stability AI's Stable Diffusion. Despite the availability of tools, the hardware required to run them remains prohibitively expensive. Researchers like Dr. Saffron Huang illustrate this challenge with her costly custom rig of eight NVIDIA RTX 4090 GPUs. The computational costs create a divide in the AI community, where organizations with resources can push advancements, while others face limitations. Environmental impacts also exacerbate inequalities, as substantial computational demands have significant carbon footprints affecting vulnerable regions.
Dr. Saffron Huang's custom rig for using Stable Diffusion consists of eight NVIDIA RTX 4090 GPUs, costing over £20,000. 'There's a profound irony here,' she states, emphasizing that while the code is freely available, the required computational resources establish a new technological aristocracy.
'We're witnessing a bifurcation of the AI community,' states Dr. Jakob Uszkoreit, co-founder of Inceptive. On one end are organizations with vast computational resources who can rapidly progress, while others have to adapt pre-trained models under severe computational constraints.
#open-source-ai #computational-resources #technological-inequality #ai-community #environmental-impact
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