Sticky Routing: Training MoE Models for Memory-Efficient Inference
📰 ArXiv cs.AI
Learn how to train MoE models for memory-efficient inference using Sticky Routing, a technique that reduces weight swapping between storage and memory
Action Steps
- Implement StickyMoE, a differentiable routing consistency loss to reduce expert switching
- Train MoE models using Sticky Routing to minimize weight swapping
- Evaluate the memory efficiency of StickyMoE compared to existing remedies
- Apply Sticky Routing to edge devices for improved inference performance
- Compare the results of StickyMoE with caching heuristics and router fine-tuning
Who Needs to Know This
Researchers and engineers working on MoE models and edge devices can benefit from this technique to improve inference efficiency
Key Insight
💡 Sticky Routing reduces expert switching, leading to more memory-efficient inference in MoE models
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🚀 Improve MoE model inference efficiency with Sticky Routing! 📈 Reduce weight swapping and boost performance on edge devices
Key Takeaways
Learn how to train MoE models for memory-efficient inference using Sticky Routing, a technique that reduces weight swapping between storage and memory
Full Article
Title: Sticky Routing: Training MoE Models for Memory-Efficient Inference
Abstract:
arXiv:2607.08780v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices. Existing remedies are either system-level (caching heuristics) or post-hoc (router fine-tuning), leaving the root cause unchanged during pretraining. We propose StickyMoE, a differentiable routing consistency loss t
Abstract:
arXiv:2607.08780v1 Announce Type: cross Abstract: Mixture-of-Experts (MoE) models activate only a sparse subset of experts per token, yet consecutive tokens frequently activate different experts -- causing constant weight swapping between slow storage and fast memory on edge devices. Existing remedies are either system-level (caching heuristics) or post-hoc (router fine-tuning), leaving the root cause unchanged during pretraining. We propose StickyMoE, a differentiable routing consistency loss t
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