PALS: Power-Aware LLM Serving for Mixture-of-Experts Models
The paper presents PALS, a power-aware runtime for serving large language models (LLMs) that optimizes GPU power alongside software parameters. This system aims to enhance energy efficiency while meeting throughput targets without requiring model retraining. Results indicate that PALS can improve energy efficiency by up to 26.3% and significantly reduce quality of service violations under power constraints.
- ▪PALS treats GPU power caps as a controllable resource to optimize LLM serving.
- ▪The system combines offline power-performance models with a feedback-driven controller.
- ▪PALS improves energy efficiency by up to 26.3% and reduces QoS violations by 4x to 7x.
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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.21427 |
| Publication time | Fri, 22 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-22T04:02:00.009Z |
| Last seen | 2026-05-22T04:02:00.009Z |
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Computer Science > Artificial Intelligence arXiv:2605.21427 (cs) [Submitted on 20 May 2026] Title:PALS: Power-Aware LLM Serving for Mixture-of-Experts Models Authors:Can Hankendi, Rana Shahout, Minlan Yu, Ayse K. Coskun View a PDF of the paper titled PALS: Power-Aware LLM Serving for Mixture-of-Experts Models, by Can Hankendi and 3 other authors View PDF HTML (experimental) Abstract:Large language model (LLM) inference has become a dominant workload in modern data centers, driving significant GPU utilization and energy consumption. While prior systems optimize throughput and latency by batching, scheduling, and parallelism, they largely treat GPU power as a static constraint rather than a controllable resource.
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