Session

DescriptionThis session highlights advancements in optimizing large language models (LLMs) for more efficient inference and communication. The papers cover strategies such as pruning, quantization, and adaptive sparse gradient compression, along with methods to reduce communication costs and improve inference speed. These techniques address challenges in long-context processing, multi-modal inference, and chip-level optimizations, all crucial for scaling LLMs in real-world applications.