Trillion-parameter LLM local deployment: AMD has released a guide showing how to use Ryzen AI Max+ chips and a Linux kernel hack to increase each node's VRAM from 96GB to 120GB. This way, the entire cluster achieves 480GB of unified GPU memory, connected via llama.cpp RPC over Ethernet. Using 4 consumer-grade AI PCs (Ryzen AI Max+ 395 ×128GB RAM), they successfully ran Kimi K2.5 (trillion parameters, 375GB model) with distributed inference through llama.cpp RPC and AMD ROCm Flash Attention (140% performance boost). This setup offers a one-time investment of $8-12K compared to ongoing cloud API fees, with fully localized data, OpenAI API compatibility, and an economical, privacy-controlled alternative for prototyping, private enterprise deployment, or research experiments. https://www.(amd.com)/en/developer/resources/technical-articles/2026/how-to-run-a-one-trillion-parameter-llm-locally-an-amd.html