Third-party cyber evaluations involving OpenAI models
openai.com·13h ago
AI Research
arXiv CS.AI·23d ago·5 min read
Director: Accelerating Distributed MoE Serving via Online Proactive Expert Placement
TL;DR
Distributed Mixture of Experts (MoE) systems face challenges in expert placement and latency. Director introduces an online proactive expert placement strategy that optimizes resource allocation dynamically.
✦ Why It Matters
Implement Director to optimize expert placement in your distributed MoE systems for improved response times.
Key Takeaways
- Adopt online proactive expert placement for dynamic request handling.
- Utilize lightweight predictors to forecast expert activation patterns.
- Implement near-zero downtime migrations during compute-bound phases.
- Leverage relaxation-based optimization for efficient expert placement.
How It Works
Director uses a prediction-driven approach to optimize expert placement in real-time. It employs either a lightweight cascaded predictor or a low-bit quantized replica to anticipate which experts will be activated by incoming requests.
The system then migrates experts during compute-bound phases to ensure minimal disruption, utilizing a relaxation-based optimizer that operates efficiently under capacity constraints.