NVIDIA GRAPHICS CARD RANKING SOFTWARE
We're also using different Stable Diffusion models, due to the choice of software projects.
Finally, on Intel GPUs, even though the ultimate performance seems to line up decently with the AMD options, in practice the time to render is substantially longer - it takes 5–10 seconds before the actual generation task kicks off, and probably a lot of extra background stuff is happening that slows it down. Nod.ai let us know they're still working on 'tuned' models for RDNA 2, which should boost performance quite a bit (potentially double) once they're available. The AMD results are also a bit of a mixed bag: RDNA 3 GPUs perform very well while the RDNA 2 GPUs seem rather mediocre. RTX 40-series results meanwhile were lower initially, but George SV8ARJ provided this fix, where replacing the PyTorch CUDA DLLs gave a healthy boost to performance. We're relatively confident that the Nvidia 30-series tests do a good job of extracting close to optimal performance - particularly when xformers is enabled, which provides an additional ~20% boost in performance (though at reduced precision that may affect quality).
NVIDIA GRAPHICS CARD RANKING CODE
We didn't code any of these tools, but we did look for stuff that was easy to get running (under Windows) that also seemed to be reasonably optimized. Getting Intel's Arc GPUs running was a bit more difficult, due to lack of support, but Stable Diffusion OpenVINO gave us some very basic functionality.ĭisclaimers are in order.
AMD GPUs were tested using Nod.ai's Shark version - we checked performance on Nvidia GPUs (in both Vulkan and CUDA modes) and found it was. For Nvidia, we opted for Automatic 1111's webui version it performed best, had more options, and was easy to get running. We ended up using three different Stable Diffusion projects for our testing, mostly because no single package worked on every GPU. But that doesn't mean you can't get Stable Diffusion running on the other GPUs. The short summary is that Nvidia's GPUs rule the roost, with most software designed using CUDA and other Nvidia toolsets. 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. 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. But how fast are consumer GPUs for doing AI inference? 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. Current conformance status can be found at 8 GPU supports DX 12 API, Hardware Feature Level 12_1Ģ Includes directly attached displays and displays connected through DisplayPort 1.2 hubsħ Product is based on a published Khronos Specification, and is expected to pass the Khronos Conformance Testing Process when available.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. * The GP100 is VR Ready, but does not support simultaneous multiprojection or any software features that require this functionality.Ħ Supported via GPUDirect for Video enabled 3rd party I/O boardsħ Product is based on a published Khronos Specification, and is expected to pass the Khronos Conformance Testing Process when available. NVIDIA® nView® Desktop Management Technology
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