Abstract:
Conventional large language models (LLMs) have large parameter sets, making inference costly and energy-intensive; mixture-of-experts (MoEs) mitigates this by activating fewer weights per token, but fine-grained MoE LLMs still face runtime workload variability, inefficient conventional scheduling, and high bandwidth memory (HBM) loading energy/bandwidth demands. A3D-MoE addresses these with 3…