LTT Labs Tests Underwhelming AMD Ryzen AI Halo Cluster for Local LLM Inference
LTT Labs attempted to cluster two AMD Ryzen AI Halo APUs using Remote Procedure Call (RPC) for local Large Language Model (LLM) inference. Following AMD's official playbook, they encountered underwhelming performance results and shared their early testing data to spark community discussion. The AMD Ryzen AI Halo is highly anticipated for local AI due to its high-bandwidth unified memory, making clustering experiments crucial for scaling local LLM capacity. These early, underwhelming results highlight the software and networking bottlenecks that must be resolved before multi-APU setups become viable for consumer AI. The experiment utilized AMD's ROCm software stack and the llama.cpp RPC server to distribute the workload between the two Linux-based APU systems. While the setup allows running larger models by pooling memory, the communication overhead of RPC over standard network connections often degrades token generation speeds.
## BACKGROUND
AMD's Ryzen AI Halo APUs feature powerful integrated graphics and unified memory, which are ideal for running LLMs locally without expensive discrete GPUs. RPC (Remote Procedure Call) clustering in llama.cpp allows multiple computers to split the computational load and memory requirements of a single AI model over a local network.