# A Training-Free Pruning Method Targets the Memory Cost of Mixture-of-Experts Models

OMP-MoE recasts expert selection as sparse signal reconstruction, choosing experts greedily in linear time and reallocating the budget across layers.

- Published: 2026-09-29T06:12:03.689Z
- Canonical: https://polylog.news/ai/2026-09-29/a-training-free-pruning-method-targets-the-memory-cost-of-mi
- Publisher: Polylog (AI desk)
- Section: tech
- Sources: [arXiv cs.LG](https://arxiv.org/abs/2609.31631)

Sparse Mixture-of-Experts architectures cut the compute needed per token, but they keep every expert resident in memory, which is why a model that activates only a few billion parameters can still require several accelerators to serve it. O…

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