arXiv:2606.09886v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) large language models achieve strong quality with low per-token compute, yet their deployment is often limited by the memory wall: the full expert pool must remain resident to support token-dependent routing. Expert pruning is a direct remedy, but prior criteria typically score experts independently and overlook that MoE inference is inherently \emph{coalitional}, where outputs arise from routed top-$k$ expert combinations. We propose \textbf{SHAPE}, a task-driven pruning framework that explicitly models \emph{intr
The increasing scale of MoE LLMs necessitates more efficient deployment strategies, making memory optimization a critical area of research as models become larger.
This development addresses a key bottleneck in the deployment of large, efficient AI models, potentially expanding their accessibility and utility across various applications.
Expert pruning in MoE LLMs can now be performed more effectively by considering expert coalitions, leading to better memory management and potentially more performant sparse models.
- · AI developers
- · Cloud providers
- · Companies using LLMs
- · N/A
More efficient and cost-effective deployment of powerful large language models.
Broader adoption of MoE architectures in commercial products due to reduced operational costs.
Acceleration of AI research and deployment in resource-constrained environments, fostering new applications.
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Read at arXiv cs.LG