Building the Best Computer for Artificial Intelligence and Machine Learning.
Building a computer for artificial intelligence or machine learning differs significantly from constructing a gaming machine. Here are some key tips to ensure you make the right decisions.
One of the best ways to protect your data against information theft or privacy invasions when using artificial intelligence or machine learning is to run the model on your own hardware. Depending on the model you choose, it is not necessary to have an extremely powerful system, although having good hardware helps. Below are recommendations for assembling a suitable PC for artificial intelligence and machine learning tasks, ensuring that your data remains secure and private.
PC Needs for AI
AI computers are not very different from high-performance PCs designed for other uses; however, they have specific necessary features that make their assembly different from that of a PC designed for gaming. Although major processor manufacturers have mentioned the efficiency of neural processors, they only offer tens of TOPS (trillions of operations per second). This may seem like a lot, but a Nvidia RTX 4090 can exceed 1,300 TOPS, demonstrating that for AI tasks, the GPU is much more crucial than the CPU.
Therefore, it is recommended to choose a powerful graphics card with a large amount of video memory, as well as sufficient system memory and fast local storage. It is also essential to have a high-end motherboard, which, while it does not directly improve AI performance, provides stable power flow to the components and allows for the connection of multiple graphics cards if necessary.
Processor
The processor is usually the centerpiece of a PC, whether for gaming, office work, or video editing, and while it also plays a role in an AI PC, it is not the most critical component. A modern processor with many cores and good future upgradeability is suggested. A recommended option is the AMD Ryzen 9950X, which offers 16 cores and supports 32 threads. If you’re looking for something more economical, the previous generation 7950X remains efficient and costs about $100 less.
Motherboard
The motherboard may not be the most exciting part of building a PC, but in AI and machine learning systems, it is crucial. It should have strong and stable VRMs to handle the required power and preferably support for PCI Express 5, along with the capability to connect multiple graphics cards. While more economical models can be chosen, it is important to ensure that they meet the required specifications and are compatible with the processor.
Graphics Card
If there is one component worth investing in for your AI PC, it is the graphics card. It should have sufficient VRAM to store the model and the parallel processing capacity needed to run complex tasks. If budget is a factor, an affordable option would be the Nvidia RTX 3060 with 12GB, priced around $300. However, for optimal performance in AI training, high-end models such as the RTX 5090 would be ideal.
Memory
Regarding memory, it is advisable to have a generous and high-speed amount, although it will not have the greatest impact on performance. A good option could be a 64GB kit at 6400 MHz from recognized brands like Corsair or Kingston.
Storage
To handle all the necessary training data, it is vital to have fast storage. Nowadays, it is possible to acquire multiple terabytes of PCIe 5 SSDs at reasonable prices, which makes accessing and managing large volumes of data easier.
Power Supply
Finally, do not skimp on the power supply. It is advisable to purchase a model over 1,200W with Titanium or Platinum certification from reputable brands like EVGA or Corsair, ensuring the long-term health of the system.
Once you have all the recommended hardware, either by assembling it yourself or having someone with experience do the assembly, you will be able to take advantage of a powerful AI and machine learning PC.
