Yeah, honestly the previous post is spot on about the gaming side of things, but for compute stuff it’s still a bit of a maze. I recently built a dual-GPU setup for some machine learning projects and tbh, I learned that the motherboard price is only half the battle. I initially tried to save money on a mid-range board, but it ended up being way more expensive in the long run because of the extra cooling I had to buy. Here’s what I ran into with my current setup:
* Power draw: I totally underestimated the PSU needs; those transient spikes are no joke when both cards are pinned.
* Thermal throttling: My cards were basically choking each other because the slots were too close, so I had to spend extra on high-airflow fans and a bigger case.
* Bifurcation: Figuring out if the lanes are actually x8/x8 or if the second slot is just an x4 through the chipset was a huge headache. Anyway, if ur on a budget, dont just look at the slot count. I spent weeks troubleshooting crashes because I didn't realize how my board was splitting bandwidth when both cards were active. Tbh, checking the block diagram in the manual is the only way to be sure before you buy.

For multi-GPU gaming, that's largely a thing of the past due to driver and game support fading. However, some higher-end X670E and X870E motherboards (like the ASRock X670E Taichi/Taichi Lite) offer dual PCIe x8 slots. Look for boards with proper PCIe lane bifurcation and physical spacing between slots for cooling. A robust power delivery system is also crucial for stability. If you're targeting compute workloads, multi-GPU setups are still relevant and similar motherboard considerations apply.