arXiv:2607.02391v1 Announce Type: cross Abstract: Large Language Model (LLM) inference workloads are a rapidly growing contributor to data center energy consumption. Optimizing these deployments requires matching specific LLMs to the most efficient GPUs, but operators currently lack the tools to do so without exhaustively profiling each combination. While some predictive models exist, they still require profiling data and struggle to generalize to hardware unseen during training. To address this, we introduce \textit{WattGPU}, featuring two predictive models for mean GPU power draw and Inter-T
The rapid growth of LLM inference workloads is driving an urgent need for energy efficiency, making tools like WattGPU critical as data centers scale. This research directly addresses the current limitations in optimizing these energy-intensive operations.
A strategic reader should care because efficient allocation and prediction of GPU power and latency directly impact the economic viability and environmental footprint of large-scale AI deployments. This enhances strategic planning for compute infrastructure.
Operators will gain the ability to predict power and latency for LLMs on unseen GPUs without extensive profiling, enabling more informed purchasing and deployment decisions. This shifts the current ad-hoc optimization approach towards data-driven forecasting.
- · Hyperscale data centers
- · Cloud providers
- · AI model developers
- · GPU manufacturers focused on efficiency
- · Less energy-efficient data center operators
- · GPU models with poor performance-per-watt
- · Organizations without robust power management strategies
Immediate operational cost reductions for LLM inference due to optimized hardware selection.
Accelerated development and adoption of energy-efficient AI hardware and software architectures.
Enhanced competition among GPU manufacturers based on predictive efficiency metrics, potentially influencing future chip design.
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Read at arXiv cs.LG