Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference

📰 ArXiv cs.AI

Researchers analyze data movement patterns in large-scale MoE LLM inference to improve efficiency

advanced Published 6 Apr 2026

Action Steps

Analyze data movement patterns in MoE LLMs
Identify bottlenecks in multi-unit LLM serving systems
Develop forecasting models to predict data movement
Optimize data movement for efficient large-scale MoE LLM inference

Who Needs to Know This

AI engineers and researchers working on large language models can benefit from this study to optimize their models' performance and reduce data movement overhead

Key Insight

💡 Understanding data movement patterns is crucial for optimizing large-scale MoE LLM performance

Key Takeaways

Researchers analyze data movement patterns in large-scale MoE LLM inference to improve efficiency

Full Article

Title: Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference

Abstract:
arXiv:2510.05497v4 Announce Type: replace-cross Abstract: Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have recently become the frontier open weight models, achieving remarkable model capability similar to proprietary ones. But their random expert selection mechanism introduces significant data movement overhead that becomes the dominant bottleneck in multi-unit LLM serving systems. To understand the patterns underlying this data movement, we conduct comprehensive data-movem

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