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Describe the division of computational workload between the CPU and GPU in the pure-CPU MoE fallback backend, and explain the core architectural trade-off of this design.
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Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Ch.4 Platform Adaptation and Performance Evaluation - Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Expert Storage Formats and Platform Adaptation - Edge-Native Mixture-of-Experts Serving with FreeToken @ University of Michigan - Ann Arbor
Related
In the pure-CPU MoE fallback backend, what memory type hosts the expert parameters?
The pure-CPU MoE fallback backend achieves higher peak PCIe transfer bandwidth than backends utilizing pinned DMA transfers.
What platform or operating system limitations trigger the serving engine to activate the pure-CPU MoE fallback backend?
Describe the division of computational workload between the CPU and GPU in the pure-CPU MoE fallback backend, and explain the core architectural trade-off of this design.