Training advanced AI models may not require massive data centers as initially thought, offering a significant shift in resource utilization.
Recent advancements suggest that distributing the training process across numerous smaller, decentralized systems could be equally effective.
This innovative approach promises to drastically reduce the environmental impact and financial costs associated with AI development.
Ultimately, this could lead to a future where AI models are trained without needing any specialized, centralized hardware at all.
