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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.09721 |
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Table of Contents:
- 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 clusters: increasing FFN instance count fails to improve HFU as computational workload is capped by scale-out bandwidth, causing operator active time to shrink relative to the fixed latency budget. We further show that AFD's discrete node-level scaling incurs higher imbalance penalties than EP's continuous batch adjustment. Nevertheless, these limitations diminish under specific conditions: Superpod-class hardware with abundant interconnect bandwidth and models with coarse-grained experts and lower sparsity are more likely to benefit from AFD. These findings position AFD as a promising approach for specific hardware-model combinations rather than a universal solution.