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Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference
arXiv – CS AI|Zhongkai Yu, Yue Guan, Zihao Yu, Chenyang Zhou, Zhengding Hu, Shuyi Pei, Yangwook Kang, Yufei Ding, Po-An Tsai|
🤖AI Summary
Researchers analyzed data movement patterns in large-scale Mixture of Experts (MoE) language models (200B-1000B parameters) to optimize inference performance. Their findings led to architectural modifications achieving 6.6x speedups on wafer-scale GPUs and up to 1.25x improvements on existing systems through better expert placement algorithms.
Key Takeaways
- →Data movement overhead from random expert selection is the dominant bottleneck in multi-unit MoE LLM serving systems
- →Comprehensive profiling of four state-of-the-art MoE models using 24,000+ requests revealed six key optimization insights
- →Lightweight architectural modifications can achieve 6.6x average speedup across 200B-1000B parameter models on wafer-scale GPUs
- →A prefill-aware expert placement algorithm delivers up to 1.25x speedup on existing GPU systems
- →This represents the first comprehensive data-centric analysis of large-scale MoE models with publicly available profiling traces
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#moe-models#llm-inference#gpu-optimization#data-movement#expert-systems#performance#wafer-scale#architecture#open-source
Read Original →via arXiv – CS AI
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