The authors designed SlimWise to allocate expert capacity separately to the two inference phases of MoE models: prefill uses the full expert pool, while decode uses a pruned pool.
Expert pruning in MoE models can be tailored to decode phase, preserving prefill quality while boosting throughput
SlimWise framework keeps full expert pool for prefill, prunes for decode, and reuses KV cache without conversion, achieving up to 1.81x decode throughput on Qwen3.6-35B-A3B with 50% expert pruning.
Top University
Gunho Park · Kyoungho Jeun · Juntaek Oh · Byeongjun Shin · Baeseong Park · Minsoo Rhu
a2sys · KAIST
Research Digest··3 min read
The authors present SlimWise, a serving framework for mixture-of-experts (MoE) models that decouples expert pruning across prefill and decode phases.
Why this paper
From KAIST and a2sys
In one line
SlimWise improves MoE decode throughput by pruning experts only during decode while keeping full prefill.
What we could check
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- ✓Limitations stated by the authors (2 noted)
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