How PC and Mac Architecture Affects AIGC & ComfyUI

At the beginning of 2025, I discovered ComfyUI, a node-based AI image generation tool that gives designers unprecedented freedom and control. It works like Lego for AI images, you can connect different nodes to build your own complex workflows.

In this article, I’ll dive into how PC and Mac hardware differences impact AIGC (AI-generated content) image creation and ComfyUI’s performance, from my perspective as a visual designer. If you're curious about the fun and creative ways to use ComfyUI, check out my other article [Link], and for a comparison with traditional AI image tools like Midjourney, see [Link].

Understanding PC vs. Mac for AIGC: Key Differences

To see why ComfyUI runs differently on different computers, we first need to understand how PC and Mac architectures differ, and how AIGC image generation actually works.

1. Hardware Architecture

• PCs use x86 architecture and offer more GPU choices (NVIDIA & AMD).

• Macs use ARM-based Apple Silicon, where the CPU and GPU are integrated, making hardware and software work together more smoothly.

2. Operating System

• PCs mainly run Windows, which has broad driver and software support.

• Macs run macOS, with its own unique system features and app ecosystem.

3. GPU Acceleration (Why Mac Isn’t Ideal for AIGC)

Using a CUDA-compatible GPU dramatically speeds up AI image generation. But here’s the catch: Only NVIDIA GPUs support CUDA, which Macs don’t have.

To put it simply: Mac’s hardware isn’t built for AIGC. Since its GPU is integrated into the CPU, it’s great for handling single complex tasks, but not for AI image generation, which relies on massive parallel computing power.

It’s like trying to drive a sports car on an off-road track, it’s not the car’s fault, or the track’s fault, it’s just not a good match. So yeah, I’ll probably switch to a PC for AIGC. A laptop with a high-end RTX 4090 GPU costs around $3,000-$4,000. My wallet is already crying. It’s more expensive than a Nikon Z8! 😭

How AIGC Image Generation Works

AIGC image generation mainly relies on deep learning models, like Diffusion Models. These models need huge computing power, especially GPU parallel processing.

• GPU performance, memory bandwidth, and driver optimization all impact AIGC speed and quality.

• On Macs, GPU and CPU integration makes data transfer faster, but right now, Windows PCs with NVIDIA GPUs still dominate AIGC performance because of CUDA’s efficiency.

Mac Users Matter: The Design Industry Standard

I totally get why Mac’s architecture isn’t ideal for AI image generation. But please, AI software developers, don’t abandon us Mac users! I’m begging you! 🥺

Most visual designers and product designers use Macs. That’s why optimizing ComfyUI for Mac is crucial.

For example, I spent an hour just installing ComfyUI, but every time I tried to run it on my Mac, it had to launch PyTorch, which failed 2 out of 3 times. So I wasted 6 more hours troubleshooting and installing Stability Matrix’s desktop version just to get it running.

And that was just the start.

Whenever I tried to install large AI models, LoRA, ControlNet, or even the ComfyUI-Manager plugin, things got way more complicated.

Now, one-third of my daily AI learning time is spent debugging instead of creating. It’s exhausting. 😩

Honestly, as a non-coder, this feels like I’m being forced to learn coding just to use an AI tool. I probably could learn it, but it’s going to be painful and time-consuming. Let’s just say… I’ve had to resist the urge to smash my computer a few times.

What ComfyUI Needs for a Better Future

1. Native Support for Apple Silicon

• Fully utilize Apple Silicon’s GPU and Neural Engine for AI computing.

• Optimize code for better performance and lower energy consumption.

2. Better UI and User Experience

• Improve UI/UX for Mac users, make the interface more intuitive and user-friendly.

• Add Mac-native features like Quick Look previews and Touch Bar support.

3. Improved Compatibility

• Ensure ComfyUI runs smoothly on the latest macOS versions.

• Fix driver and dependency issues that make installation a nightmare.

Looking Ahead: Cross-Platform Optimization & Ecosystem Growth

Here are some small (but important) suggestions, with a little Mac-user bias 😉:

1. UI Optimization for Beginners

• Add pre-made UI features for beginners.

• Maybe turn important nodes into toolbar buttons, like Photoshop tools?

• Keep the flexible node-based system, but make it easier to organize and connect nodes.

2. Cross-Platform Performance Boost

• Make ComfyUI run well on PC, Linux, and Mac.

• Provide official cloud deployment guides, for example, can we get a step-by-step guide for setting up ComfyUI on a cloud server?

3. Cloud AI Model Installation Guide

Take the Flux model as an example. Flux is a powerful AI image model with different versions (Pro, Dev, Schnell) that produce high-quality images and respond well to text prompts.

I tried installing Flux on Google Colab to bypass hardware limits. I even paid $10 for a Colab Pro subscription for more computing power.

After 10 hours of testing, it worked at first, but then Colab kept giving me errors, and I eventually gave up. So now I’m just sticking with SDXL models. It was frustrating, but at least I tried.

With the right optimizations, ComfyUI could become an even more powerful, user-friendly, and cross-platform AIGC tool, bringing endless creative possibilities to designers worldwide.

Final Thoughts: The AI Boom and My Learning Journey

As of March 2025, we’re in an AI explosion era. Big and small AI companies are fighting for resources and market share.

• I just finished learning SDXL and Flux models, and now Flux 2.0 and SD 3.5 are already here.

• China’s DeepSeek model launched in February 2025, skipping NVIDIA’s CUDA restrictions and breaking the NVIDIA monopoly.

• Who knows? Maybe new AI art models will be based on this tech soon.

We don’t know what groundbreaking tech will come next, but as a curious designer, I’ll keep learning and updating my knowledge, even if it means struggling through coding.

I feel excited but also a little lost, carefully embracing this new AI-driven world.

Shoutouts & Gratitude

Finally, huge thanks to my AI learning companions:

• My partner, Steven Dee, for technical guidance and material support. As a PC user and 3D/AI Design Expert, he sees things very differently from me as a Visual Designer. Thanks for challenging my perspective so I don’t get too biased!

• My Product Designer friend, Tina Tsung, who inspired me to write this AI review.

You guys are amazing. Sending you virtual heart emojis! 💖