User-Assisted Collaborative Distributed Inference for Efficient QoS-Aware Autoscaling
Quick summary
arXiv:2608.11840v1 Announce Type: cross Abstract: Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand. We propose a collaborative distributed inference system combining dedicated infrastructure with resources contributed by service users. Dedicated resources provide baseline capacity for maintaining quality of service (QoS), while volunteered resources absorb increasing demand without proportional growth in centralized infrastructure. To capture stochastic and dynamic interactions among users, resou
Key takeaways
- arXiv:2608.11840v1 Announce Type: cross Abstract: Growing demand for artificial intelligence (AI) inference services requires scalable infrastructure, yet centralized serving costs rise with demand.
- We propose a collaborative distributed inference system combining dedicated infrastructure with resources contributed by service users.
- Dedicated resources provide baseline capacity for maintaining quality of service (QoS), while volunteered resources absorb increasing demand without proportional growth in centralized infrastructure.
Why it matters
AI progress is not only a software story. Chips, data centers and energy decisions help determine which models can operate economically and what end users ultimately pay.
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