A detailed guide demonstrates running a one trillion-parameter Kimi K2.5 LLM locally using four AMD Ryzen AI Max+ Framework Desktops. This setup leverages llama.cpp with ROCm support for distributed inference, enabling large-scale AI workloads without cloud dependency. The Kimi K2.5 model, renowned for its open-source coding and multimodal capabilities, is partitioned across the cluster, simulating a unified accelerator.

Key steps include configuring BIOS for extended VRAM via TTM parameters and installing ROCm 7.0.2, transforming each Ryzen AI Max+ into a node in a cohesive system. The architecture allows seamless RPC communication between nodes, optimizing tensor transfers and synchronization for efficient inference. This practical implementation showcases the hardware's potential for accessible, high-performance local AI deployment.