数据中心GPU能耗是AI部署的隐形杀手，PALS把功耗从硬约束变成可调参数，做LLM服务部署的团队可以直接在vLLM上集成，省电又保性能，值得一试。
PALS是一个针对大语言模型推理的功耗感知运行时系统，将GPU功耗上限作为可调控制参数，与批处理大小等软件参数联合优化。该系统结合轻量级离线功耗性能模型和反馈驱动控制器，在满足吞吐量目标的同时最大化能效。在vLLM框架中实现，无需模型重训练或API更改。在多GPU系统上，针对稠密和混合专家模型，PALS能效提升最高26.3%，功耗约束下服务质量违规减少4到7倍。这展示了将功耗控制直接集成到LLM推理运行时中的潜力，可实现能效比例和电网交互式AI系统。
PALS: Power-Aware LLM Serving for Mixture-of-Experts Models
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. In this paper, we present a power-aware runtime for LLM serving, PALS, that treats GPU power caps as a first-class control knob and jointly optimizes them with software parameters such as batch size. The system combines lightweight offline power-performance models with a feedback-driven controller to select configurations that satisfy throughput targets while maximizing energy efficiency. We implement PALS within an existing LLM serving framework, vLLM, demonstrating that it requires no model retraining or API changes. Across multi-GPU systems and both dense and mixture-of-experts (MoE) models, PALS improves energy efficiency by up to 26.3%, reduces QoS violations by 4x to 7x under power constraints, and tracks dynamic power budgets. These results highlight the potential of integrating power control directly into LLM inference runtimes, enabling energy-proportional and grid-interactive AI systems.