MELINOE: Fine-Tuning Enables Memory-Efficient Inference for Mixture-of-Experts Models

2026-02-13 · Source: cs.LG updates on arXiv.org · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, quick

Summary

MELINOE is a novel fine-tuning method designed to enhance memory-efficient inference for Mixture-of-Experts (MoE) models, which typically face memory bottlenecks despite their computational efficiency due to large overall parameter counts. Existing solutions, such as offloading experts to CPU memory, incur significant I/O latency during transfer. MELINOE addresses this by fine-tuning MoE models to activate a smaller, more consistent set of preferred experts per sequence. By caching these frequently used experts in GPU memory, the method substantially reduces expert churn and CPU-GPU transfer overhead. This approach boosts throughput by 1.2-3x over efficient baselines and up to 14.7x compared to transfer-heavy baselines, all while maintaining or improving downstream task performance.

Key takeaway

For NLP Engineers deploying Mixture-of-Experts models in resource-constrained environments, MELINOE offers a practical solution to overcome memory bottlenecks. By fine-tuning your MoE models to prefer fewer experts, you can achieve substantial throughput improvements (1.2-14.7x) without sacrificing performance. Consider integrating this fine-tuning step into your model deployment pipeline to optimize inference efficiency and reduce operational costs.

Key insights

Fine-tuning MoE models to prefer fewer experts significantly reduces memory transfer overhead during inference.

Principles

• Reduce expert churn to minimize I/O latency.
• Cache preferred experts in GPU memory.

Method

MELINOE fine-tunes MoE models to concentrate expert activation, allowing a smaller, consistent set of experts to be cached in GPU memory, thereby reducing CPU-GPU transfer overhead.

In practice

• Apply fine-tuning to existing MoE models.
• Prioritize expert caching based on activation frequency.

Topics

• Mixture-of-Experts
• Memory-Efficient Inference
• Model Fine-Tuning
• GPU Memory Optimization
• Inference Throughput

Best for: NLP Engineer, AI Scientist, Research Scientist, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.LG updates on arXiv.org.