SpaceMoE partitions MoE layers across orbiting satellite subnets in a ring and optimizes expert placement by activation probability and path latency, yielding at least 3x lower inference latency in thousand-satellite simulations versus random baselines.
Optimizing mixture-of-experts inference time combining model deployment and communication scheduling
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Comprehensive profiling of expert selection in frontier MoE models reveals temporal and spatial patterns that enable 6.6x speedup on wafer-scale GPUs and 1.25x on existing systems via targeted optimizations.
citing papers explorer
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SpaceMoE: Realizing Distributed Mixture-of-Experts Inference over Space Networks
SpaceMoE partitions MoE layers across orbiting satellite subnets in a ring and optimizes expert placement by activation probability and path latency, yielding at least 3x lower inference latency in thousand-satellite simulations versus random baselines.
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Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
Comprehensive profiling of expert selection in frontier MoE models reveals temporal and spatial patterns that enable 6.6x speedup on wafer-scale GPUs and 1.25x on existing systems via targeted optimizations.