arXiv:2605.21427v1 Announce Type: new 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. 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 combin
As LLM inference scales dramatically, the energy consumption and GPU utilization become critical bottlenecks, necessitating innovations in power management.
Optimizing GPU power consumption for LLMs directly addresses a major operational cost and environmental concern for data centers, impacting the scalability and affordability of AI.
GPU power caps are now being treated as first-class optimization parameters, moving beyond static constraints to dynamic management alongside software settings like batch size.
- · Hyperscale data centers
- · LLM providers
- · GPU manufacturers innovating power efficiency
- · Cloud computing providers
- · Data centers with inefficient power management
- · LLM providers with unoptimized infrastructure
- · Legacy cooling solutions
Reduced operational costs and carbon footprint for large-scale AI inference facilities.
Accelerated deployment and accessibility of sophisticated LLMs due to improved cost-efficiency.
Increased competition and innovation in power-aware hardware and software solutions across the AI compute stack.
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Read at arXiv cs.AI