llama.cpp dual-GPU setup hits PCIe wall

This is mostly a capacity play, not a free performance upgrade. If the goal is to fit bigger quants, dual-GPU can help; if the goal is faster tokens/sec, the interconnect is likely to become the bottleneck.

• –llama.cpp does support multi-GPU on CUDA, including row split mode, but upstream docs say it is relatively poorly optimized and only helps when the interconnect is fast enough.
• –A mixed 5080 + 5060 Ti setup can work for local inference, but it adds split planning, model placement choices, and more room for performance surprises than a single larger card.
• –A second slot running through the chipset at PCIe 4.0 x4 is fine for expansion, but it is not the kind of link you want if the GPUs need to talk often during generation.
• –vLLM has stronger documented tensor/pipeline parallel support, but it is more of a serving stack than a drop-in answer for casual GGUF experimentation.
• –For Qwen3.5 27B- and 31B-class workloads, the cleanest path is usually the simplest one: one bigger-VRAM GPU, or accept that dual-GPU buys you fit more than speed.