DriftSched: Adaptive QoS-Aware Scheduling under Runtime Token Drift for Multi-Tenant GPU Inference
arXiv:2606.02982v1 Announce Type: cross Abstract: The rapid growth of large language model (LLM) inference services has increased the demand for efficient multi-tenant GPU scheduling. While modern inference runtimes such as vLLM improve throughput through continuous batching and optimized memory management, accurately estimating the runtime cost of heterogeneous inference requests remains a significant challenge. In practice, observed output lengths often deviate from admission-time estimates, creating runtime token drift that can lead to workload misclassification, queue imbalance, increased
The rapid growth of large language model (LLM) inference services and the increasing demand for efficient multi-tenant GPU scheduling highlight the immediate need for improved resource management solutions.
Efficient resource scheduling directly impacts the scalability and cost-efficiency of AI inference, a crucial component for widespread AI adoption and service delivery.
This advancement aims to mitigate the challenges of runtime token drift, leading to more stable and predictable performance in multi-tenant GPU environments for LLMs.
- · AI service providers
- · Cloud infrastructure companies
- · GPU manufacturers
- · LLM developers
- · AI inference providers with inefficient scheduling
- · Companies with high GPU idle times
Improved utilization and reduced operational costs for large-scale AI inference deployments.
Faster and more reliable AI services become more accessible and economically viable for a wider range of applications.
This efficiency could accelerate the development and deployment of more complex and resource-intensive AI models, further driving innovation.
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Read at arXiv cs.LG