Algorithms and machine learning models require an immense amount of computational power. Regular office or gaming computers simply do not meet the performance requirements, nor do they offer sufficient RAM and video memory. Collaborating with HYPERPC, an international manufacturer of professional workstation PCs, we will explain the essential components needed for a stable and efficient machine learning workstation.
Table of Contents
Processor
Unlike video games, machine learning algorithms are not demanding on the architecture and power of processors. The primary focus is on the number of cores and threads available. The more cores and threads, the better the performance for machine learning tasks. Therefore, AI workstations typically employ server and professional processors like Intel Xeon and AMD Ryzen Threadripper, which are designed for high-performance computing tasks.
However, these CPUs come with a hefty price tag. Additionally, the motherboards compatible with these processors are also expensive. For those who cannot afford server-grade CPUs, desktop processors like the Intel Core 13th and 14th generations are a viable alternative. Although these desktop CPUs have fewer cores and threads, making AI tasks more challenging and slower, they still provide a cost-effective solution for building a workstation.
Graphics Card
Regular gaming graphics cards, such as the NVIDIA GeForce series, can be utilized for AI work. These GPUs support all current libraries and frameworks, including technologies like CUDA, cuDNN, and tensor cores, which are essential for machine learning. The critical factor is having a large amount of video memory, preferably 16GB or more. Suitable NVIDIA GeForce models include:
- RTX 4060 Ti [16GB];
- RTX 4070 Ti Super;
- RTX 4080;
- RTX 4080 Super;
- RTX 4090.
Despite their capabilities, the NVIDIA Quadro series is the optimal choice for AI work. These cards are engineered specifically for professional tasks, including machine learning. NVIDIA Quadro GPUs offer unique advantages over GeForce cards, such as:
- unique drivers tailored for professional applications;
- hardware support for OpenGL;
- certification from software developers;
- specific video output configurations for professional displays;
- the use of ECC memory with error correction;
- standardized components for power systems and GPU settings.
The NVIDIA Quadro RTX A6000 is the pinnacle of GPUs for machine learning, capable of handling large datasets and the most complex models.
RAM
Gaming computers typically do not require more than 32GB of RAM, as modern games do not utilize more than that. In contrast, AI workstations require significantly more RAM. The exact amount needed depends on the complexity of the models and the frameworks being used.
For smaller models, 32GB to 64GB of RAM might be sufficient. However, for more complex algorithms, even 128GB of RAM may fall short. It is crucial to assess your specific needs accurately to avoid unnecessary expenses, as the cost of a workstation can vary greatly based on the amount of RAM.
SSD Drive
Traditional hard drives are too slow for efficient AI work. They cannot provide the fast loading speeds required for transferring files into the computer’s RAM and video memory. This limitation significantly reduces the efficiency of the PC during machine learning tasks. To mitigate this issue, AI workstations are equipped with high-speed SSDs featuring PCI-E interfaces.
Beyond speed, the reliability and durability of SSDs are paramount. Losing critical data due to a drive failure is unacceptable. Consequently, the most reliable SSDs, such as those from Samsung, are preferred for workstations. Samsung SSDs are known for their exceptional reliability and longevity, in addition to their superior speed performance.
Cooling System
The components used in workstations generate a substantial amount of heat due to their high performance. Effective heat dissipation is essential to prevent throttling and protect the hardware. Professional PCs, therefore, utilize advanced liquid and air cooling systems.
Conventional tower radiators are inadequate for the heat output of server and multi-core processors. Thus, liquid cooling systems are installed in workstations to effectively manage the heat from even the most powerful CPUs.
For workstations equipped with multiple graphics cards, custom liquid cooling systems are necessary. Standard factory radiators and fans cannot accommodate multiple GPUs within a single PC. Custom liquid cooling systems are designed and assembled by hand, tailored to each unique workstation.
Additionally, multiple case fans are installed in professional PCs to expel hot air and draw in cool air, ensuring optimal temperature regulation. To minimize noise, high-quality coolers with magnetic and hydrodynamic bearings are used, ensuring quiet operation even at high speeds.
Conclusion
AI workstations differ significantly from regular office and gaming computers. Key components of an AI workstation include:
- processors with a large number of cores and threads;
- NVIDIA GeForce and Quadro graphics cards with substantial video memory;
- large volume DDR5 RAM modules;
- high-speed Samsung PCI-E SSD drives;
- highly efficient cooling systems.
These components ensure that the workstation can handle the demanding tasks required for machine learning, providing both stability and efficiency. Whether you are building a budget-friendly setup or a high-end professional workstation, understanding these requirements is crucial for achieving optimal performance in AI applications.
