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[2411.08982] Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection
Summary
The paper introduces Lynx, a system designed to enhance the efficiency of Mixture-of-Expert (MoE) models by implementing dynamic batch-aware expert selection, achieving significant improvements in throughput and accuracy.
Why It Matters
As AI models grow in complexity, optimizing their performance during inference becomes crucial. Lynx addresses the inefficiencies in MoE models, which are increasingly used in foundational AI systems, thus offering a solution that can improve both speed and accuracy across various tasks.
Key Takeaways
- Lynx improves MoE inference efficiency through dynamic expert selection.
- Achieves up to 1.23x throughput improvement and up to 4% accuracy gain.
- Compatible with existing techniques, enhancing their performance by up to 1.38x.
- Addresses the tension between batching and selective parameter activation in MoEs.
- Demonstrated effectiveness across multiple state-of-the-art model families.
Computer Science > Machine Learning arXiv:2411.08982 (cs) [Submitted on 13 Nov 2024 (v1), last revised 13 Feb 2026 (this version, v2)] Title:Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection Authors:Vima Gupta, Jae Hyung Ju, Kartik Sinha, Ada Gavrilovska, Anand Padmanabha Iyer View a PDF of the paper titled Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection, by Vima Gupta and 4 other authors View PDF HTML (experimental) Abstract:Selective parameter activation provided by Mixture-of-Expert (MoE) models have made them a popular choice in modern foundational models. However, MoEs face a fundamental tension when employed for serving. Batching, critical for performance in serving, forces the activation of all experts, thereby negating MoEs' benefits and exacerbating memory bandwidth bottlenecks. Existing work on efficient MoE inference are unable to resolve this tension even with extensive workload-specific tuning. We present LYNX, a system that enables efficient MoE inference in a workload-agnostic fashion. Exploiting several key observations that we uncover in this work, LYNX provides a light-weight run-time dynamic expert remapping technique that depends only on information already available in the models. Our evaluation of LYNX on four state-of-the-art model families across nine benchmarks shows that it achieves up to 1.23x improvement in throughput while simultaneously improving accuracy by up to 4% in the ...
