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<\/figure>\n\n\n\nThe International Energy Agency notes that energy consumption per AI task has dropped remarkably fast thanks to hardware and software improvements. At the same time, models capable of performing specific tasks using far fewer resources are emerging.<\/p>\n\n\n\n
This raises an interesting point. At first, it seemed we needed to fire a cannon to solve any problem. Now, we are discovering that some issues can be perfectly resolved with a rifle, and others even with a pistol.<\/p>\n\n\n\n
Not every question requires millions of calculations performed in fractions of a second. If we want an immediate response, we need significant processing power at the ready. But if we can wait a few seconds\u2014or even minutes\u2014the requirements change drastically.<\/p>\n\n\n\n
This opens the door to local AI. An increasing number of models can run directly on a computer, eliminating the need to constantly send our data to a massive data center. Chip manufacturers are developing processors specifically designed to run artificial intelligence on personal devices.<\/p>\n\n\n\n
Relatively compact systems capable of running AI models locally are already available for a few thousand dollars. This could be a game-changer. Local AI also offers a crucial advantage: privacy. Data can remain on the computer itself, without needing to be sent to an external server.<\/p>\n\n\n\n
I personally use an open-source AI model installed on my computer for a specific application. It isn't particularly fast; sometimes I have to wait 20 to 30 minutes for a result. But I don't mind\u2014while the computer works, I attend to other tasks. In return, I keep my data under my own control.<\/p>\n\n\n\n
Of course, this doesn't mean large data centers will disappear. They will remain essential for training massive models, serving millions of simultaneous users, and running applications that require immense computing power.<\/p>\n\n\n\n
But perhaps the right question isn't whether we need data centers, but rather how many we need and what tasks they should actually perform. The evolution of open-source models, specialized chips, and locally run AI could shift the balance.<\/p>\n\n\n\n
China is the country pushing hardest for open-source AI, while the United States and other nations are accelerating this evolution; smaller, cheaper, and more efficient models are emerging every few months. That is why I believe we are entering a new era.<\/p>\n\n\n\n
In the coming years, we may discover that not everything needs to be in the cloud and that many AI tasks can be performed directly on our own computers.<\/p>\n\n\n\n
Perhaps the next artificial intelligence revolution won't just be about building increasingly large data centers. Perhaps it will be precisely about needing fewer data centers to do far more things.<\/p>\n\n\n\n
<\/p>","protected":false},"excerpt":{"rendered":"
Views: 0 La inteligencia artificial est\u00e1 cambiando muchas cosas y una de ellas, quiz\u00e1 menos visible para el gran p\u00fablico, es el enorme crecimiento de los centros de datos. Son las grandes instalaciones donde se concentran miles de servidores y millones de chips capaces de realizar cantidades gigantescas de c\u00e1lculos. Y alrededor de la IA […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[128,147,174,153,135],"tags":[],"class_list":["post-4950","post","type-post","status-publish","format-standard","hentry","category-artificial-intelligence","category-electronics","category-energy","category-industry","category-technology"],"yoast_head":"\n