Workstations for Generative AI

Our generative vision workstations are built for software like Stable Diffusion, powered by NVIDIA GeForce and RTX graphics cards. These systems are also great for smaller language models. We offer other configurations tailored more toward generative AI, LLM, and machine learning development as well as servers for hosting or training large language models.

Recommended Workstations

Hardware Utilization

AI Image & Video Generation System Requirements

In general, the CPU will not play a large role in running Generative models unless the CPU is being used instead of the GPU – which is not recommended! However, when your workflow involves more than just running Generative models, the CPU could have a large impact. For example, if part of your workflow involves data collection, manipulation, or pre-processing, the CPU may be a critical component in your work pipeline. Additionally, the choice of CPU platform affects things like overall system size, memory capacity and bandwidth, PCI-Express lane count, and I/O connectivity.

Processor (CPU)

In data science there is a significant amount of effort with movement and transformation of large data sets. The CPU, with its ability to access large amounts of memory, may dominate workflows in contrast to GPU compute in ML/DL. Multi-core parallelism will depend on the task, but parallelism in data processing is often very good.

What CPU is best for creating AI-generated images and videos?

At ProX PC our team has tested generative AI models across a wide range of modern CPUs, including the Intel Core 14600K, 14700K, and 14900K, plus their Xeon W-3495X, as well as AMD’s Ryzen 7 7700X and Threadripper PRO 7985WX – and the choice of processor had no impact on image generation speed. All of these CPUs are more than capable of supporting a modern video card, which is where the bulk of this work takes place. If you want to run multiple models at the same time, then a CPU with more PCI-Express lanes like the Threadripper or Xeon will better handle the required number of graphics cards.

Do more CPU cores make generative AI workflows faster?

In a word: no. The heavy lifting of most AI applications is done on the graphics card (GPU) rather than the CPU.

Does generative AI work better with Intel or AMD CPUs?

For most consumer-level generative AI applications, the brand of CPU does not matter. However, software optimizations in niche applications may make them better suited to either Intel or AMD. Most users will know if their programs have such optimizations.

Video Card (GPU)

Memory (RAM)

Product