Building the best multi GPU motherboards for AI rigs is the biggest decision when scaling past a single accelerator, according to a 90-day benchmark of 8 boards with paired RTX 5090 and RTX 4090 cards running llama.cpp, ComfyUI and PyTorch FSDP. Consumer X870E or Z890 boards typically split to x8/x8 with two GPUs and drop further once an M.2 NVMe drive claims lanes, while workstation chipsets such as AMD WRX90, TRX50 or Intel W790/W890 expose 96 to 128 PCIe Gen 5 lanes so four or more cards run at full bandwidth simultaneously. Picks were tiered by GPU count, from dual RTX 5090s for local LLM inference to four RTX 4090s for fine-tuning. Listed boards include the ASUS ROG Crosshair X870E Glacial, ASUS ProArt Z890-CREATOR WIFI, MSI MEG X870E GODLIKE MAX, GIGABYTE X870E AORUS Xtreme AI TOP, ASUS Pro WS TRX50-SAGE WIFI, GIGABYTE TRX50 AERO D, ASUS Pro WS W790-ACE and ASUS Pro WS W890E-SAGE SE. Identical llama.cpp tensor parallel configurations on consumer and workstation boards showed 18% to 23% throughput loss.