The Model Parking Tax: Quantifying the Hidden Energy Cost of Always-On GPU Model Deployment
The AI inference industry keeps models loaded in GPU memory around the clock to avoid cold-start latency, implicitly treating idle power as a fixed cost of readiness. Yet the structure of this cost has never been empirically decomposed - and never across GPU architectures. We present the first cross-architecture measurement of idle GPU power as a function of VRAM allocation, combining 18 days of production telemetry (335,267 samples, 14 H100 GPUs) with controlled dose-response experiments on three GPU architectures spanning three memory technologies: NVIDIA H100 (HBM3, 80 GB), A100 (HBM2e, 80 GB), and L40S (GDDR6, 48 GB). We observe that idle power is piecewise constant on all three archi...
| Authors | Sai Sathvik Vadari |
|---|---|
| Primary category | cs.DC |
| Published | 15 April 2026 |
| Last updated at source | 15 April 2026 |
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