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New Director system optimizes MoE model serving, cutting latency by up to 55%

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new system called Director to optimize the serving of Mixture-of-Experts (MoE) models. This system addresses the challenges of dynamic request patterns and the cost of expert migration by employing a prediction-driven, online expert placement strategy. Director utilizes a lightweight predictor to forecast expert activation patterns for incoming requests and an online migration module that enacts changes with minimal disruption. Experiments show that Director can reduce end-to-end latency by 11-55% for popular MoE models like Mistral, DeepSeek, and Qwen compared to existing methods. AI

IMPACT This system could significantly improve the efficiency and reduce the operational costs of serving large MoE models.

RANK_REASON The cluster contains an academic paper detailing a new system for optimizing AI model serving. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New Director system optimizes MoE model serving, cutting latency by up to 55%

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The cluster contains an academic paper detailing a new system for optimizing AI model serving. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Qianli Liu, Kaibin Guo, Zicong Hong, Peng Li, Fahao Chen, Haodong Wang, Jian Lin, Song Guo ·

    Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement

    arXiv:2607.08782v1 Announce Type: cross Abstract: Expert parallelism has become the prevailing paradigm to serve Mixture-of-Experts (MoE) models. Its efficiency depends on the communication and computation latencies of the GPUs, which are linked to the placement of experts in the…

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