Attaching an extra GPU via pcie slot

The question itself is not exotic. On March 22nd, a LocalLLaMA user raised what many home AI enthusiasts have quietly wondered: can you meaningfully extend a system's inference capability by dropping a second GPU into a spare PCIe slot? Not a server rack, not a purpose-built multi-GPU rig — just a consumer or prosumer machine with room on the motherboard and an appetite for faster local model runs. The post didn't catch fire. No flood of replies, no follow-up benchmarks, no one claiming they'd already done it and here are the results. It reads like the beginning of a conversation that hasn't quite found its audience yet, someone mapping the edges of what their setup can actually do before committing to hardware they can't easily return.

If confirmed as a reliable inference pathway — meaning a second PCIe GPU genuinely adds usable compute without crippling bandwidth bottlenecks or driver headaches — the implications for the local AI community are real and quiet in equal measure. Consumer hardware already reshaped who can run serious models; a straightforward dual-GPU path would push that further, letting people run larger quantised models or split workloads without cloud dependency. The second-order effect is subtler: it lowers the cost of experimentation, which historically is where the most interesting capability discoveries happen. Not the data centres, but the person with two mid-range cards and a free weekend.

Watch for follow-up posts in LocalLLaMA or r/MachineLearning where users share actual bandwidth and throughput numbers from dual-GPU consumer setups — that empirical data, if it appears, would quickly tell you whether this is a genuine upgrade path or a frustrating half-measure.

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