Can expert caching mechanisms in MoE models be optimized to reduce CPU-GPU memory transfer overhead while main

Abstract

Abstract: Among parallel decoding paradigms, diffusion large language models (dLLMs) have emerged as a promising candidate that balances generation quality and throughput. However, their integration with Mixture-of-Experts (MoE) architectures is constrained by an expert explosion: as the number of tokens generated in parallel increases, the number of distinct experts activated grows nearly linearly. This results in substantial memory traffic that pushes inference into a memory-bound regime, negating the efficiency gains of both MoE and parallel decoding. To address this challenge, we propose Dynamic Exp

Research Question

Can expert caching mechanisms in MoE models be optimized to reduce CPU-GPU memory transfer overhead while maintaining inference throughput gains on A100/H100 GPUs?

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