Hey.

I wanted to share a free, open-source GPU benchmark tool I built specifically for measuring how NVIDIA GPUs perform under AI workloads (Stable Diffusion). Unlike typical benchmarks that focus on gaming performance, this tests how your card handles machine learning tasks.

What it measures:

• Processing speed: How many images your GPU can generate in 5 minutes

• Temperature monitoring: Both peak and average temps during sustained AI workload

• Power draw: Precise wattage consumption under load

• Other technical specs: Like VRAM, platform and more

Some interesting findings from our current data:

• RTX 4090 generated 199 images (318W, 55°C max)

• RTX 3090 generated 116 images (335W, 69°C max)

• RTX 3060 Laptop generated only 32 images (54W, 77°C max)

• A100 80GB PCIe generated 217 images (283W, 70°C max)

These results show some interesting efficiency patterns across the different NVIDIA architectures.

Super simple to use:

pip install gpu-benchmark
gpu-benchmark

The benchmark takes about 5 minutes after initial model loading.

Would love to hear your feedback or answer any questions!

GitHub: https://github.com/yachty66/gpu-benchmark
Online benchmark results: https://www.unitedcompute.ai/gpu-benchmark

What do you use to benchmark used GPU?

What tests do you use to determine whether an old GPU is still good? Most results I've found are pretty old:

• FurMark for stress test

• 3dMark for rendering

• GPU-Z to see if it's legit

Is this correct? Are they still valid?