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SlimWise: Decoupling Expert Pruning Across Prefill and Decode for Efficient MoE Serving
Mixture-of-experts (MoE) models activate few experts per token, yet batched decoding can access nearly the entire expert pool, making expert-weight traffic a major bottleneck. Expert pruning reduces this traffic, but conventional approaches also prune compute-bound prefill, sacrificing model quality
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