arXiv:2512.18725v3 Announce Type: replace Abstract: Machine learning (ML) inference serving systems can schedule requests to improve GPU utilization and to meet service level objectives (SLOs) or deadlines. However, improving GPU utilization may compromise latency-sensitive scheduling, as concurrent tasks contend for GPU resources and thereby introduce interference. Given that interference effects introduce unpredictability in scheduling, neglecting them may compromise SLO or deadline satisfaction. Nevertheless, existing interference prediction approaches remain limited in several respects, wh
The increasing complexity and scale of ML models demand more efficient and predictable inference systems, making latency a critical bottleneck for real-world AI applications.
Predictable ML inference is crucial for deploying AI reliably in sensitive applications like autonomous systems and financial trading, directly impacting performance and trust.
This research contributes to making ML inference more dependable and less prone to unexpected delays, improving system integration and meeting stringent service level objectives.
- · AI infrastructure providers
- · Cloud computing platforms
- · Companies deploying latency-sensitive AI
- · GPU manufacturers
- · Inefficient ML inference systems
- · Companies reliant on unpredictable AI deployments
Improved reliability and performance of AI services across various industries.
Accelerated adoption of AI in critical real-time applications where predictability is paramount.
Enhanced trust in AI systems leading to broader societal integration of autonomous and intelligent technologies.
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