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cs.DC2025
Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
Zhongkai Yu, Yue Guan, Zihao Yu +6
Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have recently become the frontier open-weight models, achieving remarkable model capability similar to proprietary…
cs.DC2025
Utility-Driven Speculative Decoding for Mixture-of-Experts
Anish Saxena, Po-An Tsai, Hritvik Taneja +2
GPU memory bandwidth is the main bottleneck for low-latency Large Language Model (LLM) inference. Speculative decoding leverages idle GPU compute by using a lightweight drafter to…