FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
Mixture-of-Experts (MoE) models have become mainstream for scaling language models to hundreds of billions of expert parameters. Despite sparse expert activation, existing inference engines keep all experts GPU-resident, crowding out the key-value cache in large-batch, long-output offline workloads. We present FluxMoE, which decouples experts from physical GPU residency and adapts their footprint to available memory through a new \emph{expert paging} abstraction. FluxMoE combines PagedTensor for transparent remapping, a bandwidth-balanced hierarchy spanning losslessly compressed GPU memory and host DRAM, and a budget-aware residency planner. Unlike CPU-GPU co-inference and whole-layer off...
| Authors | Qingxiu Liu, Yongchao He, Runhan Jiang et al. |
|---|---|
| Primary category | cs.LG |
| Published | 3 April 2026 |
| Last updated at source | 10 September 2026 |
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