Revealing the Challenges of Attention-FFN Disaggregation for Modern MoE Models and Hardware Systems
Guowei Liu; Hongming Li; Yaning Guo; Yongxi Lyu; Mo Zhou; Yi Liu; Zhaogeng Li; Yanpeng Wang · 2026 · arXiv
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract (excerpt)
Deploying large-scale MoE models presents challenges in memory capacity and bandwidth for expert activation. While Attention-FFN Disaggregation (AFD) has emerged as a potential architecture to decouple compute and memory resources, its performance boundaries compared to standard large-scale Expert Parallelism (EP) remain underexplored. In this paper, we conduct a systematic analysis of AFD by extending the roofline model to the communication level, correlating interconnect bandwidth, arithmetic intensity, and Hardware FLOPS Utilization (HFU). Our analysis reveals a dead zone on standard cluste
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Metadata source: arXiv