Design Methodology and Performance Trade-offs Management for Distributed and Compound AI Systems
Artificial Intelligence (AI) systems must typically satisfy service-level objectives including accuracy, latency, and cost. The prevailing model-centric approaches select a monolithic model at design time and apply identical computation regardless of input difficulty, cannot decompose tasks across specialized components, and have knowledge that is fixed at training time. During runtime, this can lead to performance degradation and increasing costs. Because the model is the main design variable, it determines the majority of system behavior, coupling operational objectives to a single design-time choice. Addressing these limitations requires shifting from model-centric to system-centric de...
| Authors | Milos Gravara, Andrija Stanisic, Stefan Nastic |
|---|---|
| Primary category | cs.DC |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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