Features of Choosing a Workstation for AI

A high-performance workstation PC is required for machine learning, not just a powerful computer. Otherwise, achieving fast and high-quality model processing will be impossible. Together with HYPERPC, a manufacturer of premium workstations, we will determine what equipment is needed for machine learning.

Graphics Card

Machine learning algorithms primarily operate using GPUs, as their architecture includes tensor cores. These are specialized units used for matrix multiplication, forming the foundation of all artificial intelligence algorithms.

The best GPUs for machine learning are produced by NVIDIA. Their graphics cards support the most frameworks, formats, and plugins. AMD and Intel accelerators lack these advantages, making them unsuitable for machine learning.

Machine learning requires a significant amount of video memory. Therefore, the following NVIDIA GPUs are commonly used in AI workstations:

• H100;
• H200;
• A100;
• RTX 4090;
• RTX 5090;
• RTX 6000 Ada.

NVIDIA GeForce RTX 4090 and 5090 are the most popular choices. They are more affordable than their counterparts and are readily available for purchase. Often, it is more cost-effective to install multiple RTX 4090s rather than a single NVIDIA H100.

Central Processor

Some frameworks operate exclusively on central processors rather than GPUs. Therefore, a workstation must also include a powerful CPU. For AI workloads, the number of cores is more important than individual core performance—more cores mean better AI algorithm execution.

Common processors in AI workstations include:

• Intel Xeon;
• AMD Epyc;
• AMD Ryzen Threadripper.

These processors offer high core and thread counts, as well as support for large amounts of RAM, which is essential for AI workloads.

RAM Capacity

RAM (Random Access Memory) plays a crucial role in machine learning, particularly when handling large datasets. If the dataset exceeds available RAM, computations slow down due to frequent disk access.

Recommended RAM sizes:

• 32 GB – minimum for small projects.
• 64 GB – optimal for most machine learning tasks.
• 128 GB or more – required for large neural networks and extensive datasets.

If a powerful GPU is available (e.g., RTX 4090), RAM demand may be lower since computations are offloaded to video memory. However, for CPU-based training or large dataset processing, investing in more RAM is recommended.

SSD Storage

Disk speed impacts data loading and processing, especially for large models and datasets. AI workstations are equipped with high-speed NVMe SSDs for optimal performance.

Recommended storage options:

• NVMe SSD (PCIe 4.0/5.0) – ideal for machine learning tasks.
• 2 TB or more – to store datasets, model weights, and training logs.
• HDD (8-16 TB) for archival storage – used for older models and datasets.

Faster data access speeds up loading, preprocessing, and training stages. Systems handling large data volumes may benefit from multiple SSDs in a RAID configuration.

Conclusion

For efficient AI and machine learning operations, a workstation should include:

• An NVIDIA graphics card (preferably 24+ GB VRAM, such as RTX 4090 or H100).
• A multi-core processor (Intel Xeon, AMD Epyc, Threadripper).
• 64-128 GB RAM (depending on workload requirements).
• A fast NVMe SSD (2 TB or more).

Such a setup ensures high performance when training neural networks, analyzing data, and working with modern AI frameworks.