Not necessarily. While powerful hardware options like high-end GPUs or TPUs can significantly speed up deep learning tasks, they aren’t always the best choice for every situation. Here’s why:

In summary, while powerful hardware can offer significant performance improvements, it’s essential to evaluate your specific needs, budget, and the nature of your projects before making a decision.

Dear Yashar Salami ,

Machine Learning (ML) tasks are diverse, and so is the hardware on which they can be computed. To better understand how to speed up the calculations, it is important to know what kind of calculations are performed. Calculations used by ML algorithms are largely vector and matrix calculations. For this reason, a suitable computational accelerator for ML applications must be able to perform these calculations.

https://mmmake.com/en/blog/hardware-for-machine-learning-the-5-best-hardware-options-in-comparison/

Regards,

Shafagat

A simple algorithm always has a higher priority than a complex one, a simple device always has a higher priority than a complex one. Another thing is that simple solutions are not enough, in which case more complex ones are used.

they aren’t always the best choice .