Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
📰 ArXiv cs.AI
Learn how Director accelerates distributed MoE serving via online proactive expert placement, improving efficiency in AI models
Action Steps
- Implement Director to proactively place experts in GPUs
- Optimize expert placement based on past request patterns
- Evaluate the communication and computation latencies of GPUs
- Apply online proactive expert placement to accelerate MoE serving
- Compare the efficiency of Director with existing expert placement methods
Who Needs to Know This
AI engineers and researchers working with Mixture-of-Experts (MoE) models can benefit from this knowledge to optimize their model serving efficiency
Key Insight
💡 Proactive expert placement can significantly improve the efficiency of MoE model serving
Share This
🚀 Accelerate distributed MoE serving with Director! 🤖
Key Takeaways
Learn how Director accelerates distributed MoE serving via online proactive expert placement, improving efficiency in AI models
Full Article
Title: Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
Abstract:
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 GPUs. Existing works for optimizing expert placement focus on leveraging past requests' expert activation patterns. However, they demonstrate deficiencies facing diverse and rapidly changing request patterns, calling fo
Abstract:
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 GPUs. Existing works for optimizing expert placement focus on leveraging past requests' expert activation patterns. However, they demonstrate deficiencies facing diverse and rapidly changing request patterns, calling fo
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