Dekube mining

[FP91G]

Background: Why distributed Al training matters
The emergence of ChatGPT has brought about the rapid development of the AI industry in the past year, with the exponential growth of large models and their parameters (GPT3 has 175B parameters, and GPT4 has reached 1.5 Trillion, and it takes $100 million to train once), while the capacity bottleneck of high-end graphics cards for AI computing is extremely high (the price of a single H100 card is nearly $40,000), resulting in a sharp increase in the time and money cost of large model training.

But on the other hand, a large amount of scattered AI computing power is idle. The vast majority of PC graphics cards are idle most of the time. Moreover, due to the transformation of Ethereum from POW to POS, a large number of GPU computing resources are released and idle too. From the point of view of economy and efficient use of resources, it is desirable to revitalize these idle GPUs.
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[Mate2237]

AI or ChatGPT has helped people to solve many things mostly developers and scholars. The parameters and the capacity also increase to meet up the new technology because if you use the old model the functionality might not be effective. So the components of the computer system have to increase to meet up the demand of this new innovative technology age. And I will like to add one of the computing component that is idle is chips.

And mining companies have to renew those things in their PCs or desktops before mining.