Artificial Intelligence and deep learning are constantly in the headlines these days, whether it be ChatGPT generating poor advice, self-driving cars, artists being accused of using AI, medical advice from AI, and more. Most of these tools rely on complex servers with lots of hardware for training, but using the trained network via inference can be done on your PC, using its graphics card. But how fast are consumer GPUs for doing AI inference?

We’ve benchmarked Stable Diffusion, a popular AI image creator, on the latest Nvidia, AMD, and even Intel GPUs to see how they stack up. If you’ve by chance tried to get Stable Diffusion up and running on your own PC, you may have some inkling of how complex — or simple! — that can be. The short summary is that Nvidia’s GPUs rule the roost, with most software designed using CUDA and other Nvidia toolsets. But that doesn’t mean you can’t get Stable Diffusion running on the other GPUs.

We ended up using three different Stable Diffusion projects for our testing, mostly because no single package worked on every GPU. For Nvidia, we opted for Automatic 1111’s webui version (opens in new tab); it performed best, had more options, and was easy to get running. AMD GPUs were tested using Nod.ai’s Shark version (opens in new tab), and we are also testing (in Vulkan mode) on the Nvidia GPUs and will have an update shortly. Getting Intel’s Arc GPUs running was a bit more difficult, due to lack of support, but Stable Diffusion OpenVINO (opens…