Author

Baeseong Park

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#machine learning Feb 2026

PrefillShare: A Shared Prefill Module for KV Reuse in Multi-LLM Disaggregated Serving

This work proposes PrefillShare, a novel algorithm that enables sharing the prefill stage across multiple fine-tuned models in a disaggregated setting and introduces a routing mechanism that enables effective prefill sharing in a vLLM-based disaggregated system.

Sunghyeon Woo, Hoseung Kim, S. Shim et al. · arXiv.org · 3 citations

#artificial intelligence Preprint Sep 2026

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 for little throughp...

Gunho Park, Kyoungho Jeun, Juntaek Oh et al. · 0 citations