arXiv:2606.00735v1 Announce Type: cross Abstract: In distributed Mixture-of-Experts (MoE) inference, input-dependent token routing interacts with GPU performance variability to create persistent stragglers under synchronized execution, where the slowest GPU determines layer latency. This performance variability is inherent to modern accelerators: manufacturing variation, power limits, and thermal conditions introduce measurable execution-time differences across nominally identical GPUs. The core challenge is that MoE execution-time imbalance arises from the interaction of workload skew and har
The increasing scale and complexity of AI models, particularly Mixture-of-Experts (MoE) architectures, highlight the inherent hardware variability challenges in large-scale distributed inference systems. This paper directly addresses real-world performance bottlenecks arising from these complex interactions.
Optimizing MoE serving performance directly impacts the efficiency and cost-effectiveness of deploying large language models and other advanced AI applications, influencing the economic viability of AI-driven services and infrastructure.
This research provides a method to mitigate performance bottlenecks in large-scale AI inference by co-optimizing workload distribution and hardware characteristics, potentially leading to more efficient and reliable AI systems.
- · Hyperscalers
- · AI infrastructure providers
- · Companies deploying large AI models
- · GPU manufacturers who address variability
- · AI service providers with unoptimized infrastructure
- · Companies relying on inefficient distributed AI systems
Increased efficiency and lower operational costs for distributed AI inference, especially for MoE models.
Faster, more consistent deployment of advanced AI models across various industries, accelerating AI adoption and innovation.
Enhanced competition in AI services as performance and cost advantages become more accessible to optimized infrastructure providers, potentially centralizing large-scale AI deployment.
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Read at arXiv cs.LG