Which GPU should I choose to teach models efficiently and effectively? This question spends the sleep of many. How to do it right, so as not to overpay, over-save and then regret being ready for the future, to have hardware that is up to the task at hand. This is a very broad topic that needs to be looked at from a wider perspective.
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Deep Learning workstation technology is advancing at a breakneck pace and the algorithms it faces are updating just as fast. The biggest companies in the market are chasing the best performance, all in an effort to satisfy the needs of their customers. Manufacturers are striving to satisfy consumer expectations, and these in turn are hungry to expand the horizons of their IT capabilities. A computer for Deep Learning – for this highly unusual and specialised field – needs the highest performance, and therefore properly configured and synchronised components. The right hardware in this demanding field of Machine Learning is a top priority. That is why, my dear reader, you are here with us.
A computer for Machine Learning
If you want to build a Machine Learning computer that absorbs horizontal data chains, and has no problem shuffling validation sets quickly, this requires very precise optimisation of the workstation. A Machine Leraning computer is no slouch. This computer must not waste time on simple calculations, it has to love itself …
The right graphics card is the workhorse of our entire workstation. It plays a dominant role, is supported by the CPU and does the most important part of the work in machine learning computers. GPUs work very efficiently in the struggle to tame the processing of deep learning algorithms, as they are divided into several hundred smaller cores that do their work at an alarming rate. They are superbly vulnerable to computational load. Nevertheless, with the accompaniment of the CPU and GPU, we can get the results we want.
The first thing is that we need a GPU with tensor cores. These run much faster than their predecessor Pascal and were created for the specialised matrix mathematics used in deep machine learning. They run on average 12 times faster than their predecessor making them extremely efficient. Tensor cores affect the processing power of the GPU, which can positively take more operations per second. A graphics card with such cores is a must-have. We can find this type of solution on cards from NVIDIA in the RTX series.
When there is a problem with a graphics card
Due to the current world situation, it is still difficult to buy the electronic equipment of your dreams, especially when it comes to graphics cards. For this reason, it is useful to know about the alternative of a lease gpu. This is a type of service provided by an increasing number of companies such as hashmarket.ai, which offer gpu server rental. This means that, for a fee, it is possible to buy a certain amount of computing power for a specific period of time. This is a very convenient solution because it does not require investment in very expensive workstations. In this way, it is possible to obtain a very fast unit that requires virtually no financial input. The amount of hashrate can be selected according to your own needs, making Machine Learning as optimised as possible. As a result, many complex calculations will be able to be performed at almost instantaneous rates.
In summary, deep and machine learning are very computationally intensive activities, hence the use of GPUs. With the problem of having access to good quality graphics cards, it is a good idea to use GPU rental.
