KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving
Power has become a central bottleneck for AI inference. This problem is becoming more urgent as agentic AI emerges as a major workload class, yet prior power-management techniques focus almost entirely on single-turn LLM serving. Our analysis shows that agentic serving behaves fundamentally differently: each request carries long-lived context that evolves across tool-interleaved turns, and lowering GPU frequency can push the system into a thrashing regime where memory pressure sharply worsens both performance and power efficiency. These observations show that power optimization for agentic serving requires rethinking. We present KAIROS, a context-aware power optimization system for agenti...
| Authors | Yichao Yuan, Mosharaf Chowdhury, Nishil Talati |
|---|---|
| Primary category | cs.DC |
| Published | 17 April 2026 |
| Last updated at source | 17 April 2026 |
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