arXiv:2605.20799v1 Announce Type: cross Abstract: We present Overall FLOP Utilization (OFU), a hardware-level, precision-agnostic GPU efficiency metric for AI workloads on HPC systems, derived from two on-chip performance counters: Tensor Pipe Activity and SM clock frequency. OFU requires no application instrumentation and works across GPU generations and numeric precisions. We characterize five properties of the OFU approximation -- tile quantization, floating-point precision scaling, clock sampling noise, Tensor Core clock domains, and non-tensor undercounting -- through controlled GEMM expe
The proliferation of AI workloads demands more efficient GPU utilization, pushing the need for real-time, hardware-level metrics to optimize large-scale AI compute infrastructure.
This metric promises to significantly improve the efficiency and cost-effectiveness of large-scale AI training and inference by providing immediate, granular insight into GPU performance.
AI practitioners and HPC operators can now achieve better performance per watt and dollar, leading to more optimized cluster designs and potentially faster AI model development.
- · GPU manufacturers
- · Hyperscalers
- · AI research labs
- · HPC system integrators
- · Inefficient AI compute providers
Immediate understanding of GPU efficiency will enable dynamic workload scheduling and hardware allocation improvements in AI data centers.
Optimized GPU utilization could accelerate the development and deployment of larger, more complex AI models, influencing the pace of AI advancement.
Increased compute efficiency may reduce the environmental footprint of large AI systems, potentially impacting regulatory discussions around data center energy consumption.
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Read at arXiv cs.LG