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

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 →

• DeepSeek
• Director
• Mixture-of-Experts (MoE) models
• Qwen

• infra
• paper

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