Challenges and Research Directions for Large Language Model Inference Hardware
Summary
Ma and Patterson identify memory and interconnect as the primary bottlenecks for large language model inference, rather than compute. They propose four architecture research directions—high bandwidth flash with HBM-like bandwidth, processing-near-memory approaches, 3D memory-logic stacking, and low-latency interconnects—for scalable datacenter AI, with discussion on mobile applicability. The work guides infrastructure planners on hardware pathways to accelerate LLM inference in enterprise settings.