Cross-device hardware evaluation often assumes that if architecture rankings transfer across devices, a proxy device can support target-side model selection.
We stress-test this assumption for joint latency-energy feasibility across two public architecture families.
On NAS-Bench-201
Cross-device rank correlations are moderate, while target-comparable feasible-set overlap remains incomplete.
A faithful AdaProxy diagnostic substantially improves latency ranking, showing that the observed boundary failures are not simply due to weak adaptation.
Exact finite-sample split-conformal analysis
Exact finite-sample split-conformal analysis also exposes an evidence bottleneck: a finite one-sided 90% threshold requires at least nine calibration observations.
We then replicate the phenomenon on 10,000 GPT architectures across 13 HW-GPT-Bench devices.
Relative to an RTX3080 proxy
Relative to an RTX3080 proxy, target latency SRCC ranges from 0.951 to 0.996, yet proxy-reuse violation risk ranges from 33.3% to 100% under matched joint constraints.
These results show
These results show that rank portability, feasibility portability, and target-specific decision support are distinct evaluation objects.
Cross-device evaluations should therefore report which target environments actually support the operating point being claimed.