FluxMoE decouples MoE expert weights from persistent GPU residency via on-demand paging, achieving up to 3x throughput gains over vLLM in memory-constrained inference without accuracy loss.
Accurate expert predictions in MoE inference via cross-layer gate
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PreScope combines a layer-aware activation predictor, cross-layer prefetch scheduling, and asynchronous I/O to deliver 141% higher throughput and 74.6% lower latency for MoE inference on legacy hardware.
Prism optimizes expert placement and uses runtime migration for distributed MoE inference on heterogeneous edge GPUs, achieving up to 30.6% lower latency than baselines.
citing papers explorer
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FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving
FluxMoE decouples MoE expert weights from persistent GPU residency via on-demand paging, achieving up to 3x throughput gains over vLLM in memory-constrained inference without accuracy loss.
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LayerScope: Predictive Cross-Layer Scheduling for Efficient Multi-Batch MoE Inference on Legacy Servers
PreScope combines a layer-aware activation predictor, cross-layer prefetch scheduling, and asynchronous I/O to deliver 141% higher throughput and 74.6% lower latency for MoE inference on legacy hardware.
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Accelerating Edge Inference for Distributed MoE Models with Latency-Optimized Expert Placement
Prism optimizes expert placement and uses runtime migration for distributed MoE inference on heterogeneous edge GPUs, achieving up to 30.6% lower latency than baselines.