The Role of the CPU in Generative AI Workflows
In most generative AI applications, the CPU plays a minimal role unless it’s being used instead of the GPU—something we strongly advise against. However, if your workflow extends beyond just running generative models, the CPU can become a crucial component. Tasks such as data collection, preprocessing, and transformation rely heavily on CPU performance, especially in data science and machine learning pipelines. Additionally, the CPU platform impacts system size, memory capacity and bandwidth, PCI-Express lane availability, and overall connectivity.
Choosing the Right CPU for AI-Generated Images & Video
Our testing has shown that the CPU has little to no effect on the speed of AI-generated image and video creation. We’ve benchmarked various processors, including Intel’s Core 14600K, 14700K, and 14900K, as well as AMD’s Ryzen 7 7700X and Threadripper PRO 7985WX, and found that all are more than capable of supporting modern GPUs, which handle the bulk of AI workloads. If you plan to run multiple models simultaneously, a CPU with more PCI-Express lanes—such as AMD’s Threadripper or Intel’s Xeon—will better accommodate multiple GPUs.
Does More CPU Power Speed Up Generative AI?
In short, no. Generative AI is heavily GPU-dependent, and adding more CPU cores won’t significantly impact performance. However, if your workflow involves extensive data manipulation, multi-core performance can be beneficial.
Intel vs. AMD for Generative AI
For most users, the choice between Intel and AMD won’t impact generative AI performance. However, certain niche applications may have optimizations that favor one brand over the other. If your software has specific CPU optimizations, that could influence your decision.
The GPU: The Backbone of Generative AI
The GPU is the most critical component in a generative AI workstation, regardless of the type of output—whether image, video, voice, or text. Most AI frameworks are built around NVIDIA’s CUDA platform, though AMD’s ROCm is gaining traction.
Choosing the Best GPU for AI Workloads
The key factors when selecting a GPU for AI include:
- Total VRAM (Higher memory capacity enables larger models)
- Memory Bandwidth (Faster data transfer between GPU and memory)
- Floating Point Performance (FP16 precision is most relevant)
- NVIDIA Tensor Core Count & Generation (Latest: 4th-gen Tensor Cores)
- AMD Compute Unit Count
Recommended GPUs for Generative AI
Currently, we recommend NVIDIA’s GeForce RTX 5080 (16GB VRAM) and RTX 5090 (32GB VRAM) for most users. If your workflow demands more memory, consider stepping up to the RTX 5000 Ada (32GB VRAM) or RTX 6000 Ada (48GB VRAM)—though these come at a premium cost.
How Much VRAM Do You Need for Generative AI?
The amount of VRAM required depends on the complexity of the models you’re using. As a general rule, professional AI workloads benefit from higher VRAM capacity, ensuring smoother performance and larger dataset handling.
Do Multiple GPUs Improve Generative AI Performance?
Not necessarily. While multiple GPUs won’t generate a single image faster, they can be useful for batch processing or shared multi-user environments. For instance, four GPUs can generate four images simultaneously, but they won’t make one image render four times faster.
NVIDIA vs. AMD for Generative AI
NVIDIA currently holds the edge in AI workloads due to broader CUDA support and superior compute power. While AMD’s ROCm is an alternative, most AI applications are optimized for NVIDIA hardware.
Do You Need a Professional GPU for AI?
For most users, high-end consumer GPUs (like NVIDIA GeForce RTX cards) are sufficient. Professional workstation GPUs (like NVIDIA RTX Ada series) are only necessary for extreme workloads that require larger VRAM capacities.
Is NVLink Necessary for Multi-GPU AI Systems?
No. Modern AI workloads rarely require NVLink, and most new NVIDIA GPUs have phased out support for it.
Memory (RAM) Considerations for Generative AI
System RAM isn’t a primary performance factor for generative AI, but we generally recommend having at least twice the total VRAM in your system. If your workstation is also used for other tasks, ensure you allocate enough memory for those applications as well.
Build a Workstation Tailored for Generative AI
We design high-performance workstations built specifically for generative AI, LLMs, and deep learning. Whether you’re creating AI-generated art, training custom models, or running complex inference workloads, we’ll help you configure a system that meets your needs.
Need expert guidance? Our team is here to help. Get in touch with our technical consultants today.
