Power Side-Channel Identification of Expert Activations in Mixture-of-Experts Language Models

GPUs are central to modern AI infrastructure, but their physical side-channel leakage has received little scrutiny in comparison with CPUs or dedicated secure hardware. This report studies whether board-level power measurements from an NVIDIA H100 can reveal which experts activate during Mixture-of-Experts (MoE) language-model inference. Using a ChipWhisperer ADC trace from GPT-OSS-20B layer-2 decode-time expert windows, the strongest defensible strict waveform-only classifier reaches $24.7 \%$ top- 1 and $63.2 \%$ top- 5 accuracy over 32 experts, compared with a $3.1 \%$ random baseline. A controlled forced-expert harness reaches much higher accuracy, confirming that the sensor can observe expert-dependent work when the signal is repeated and isolated. However, natural decode traces remain noisy, imbalanced, and context-dependent. The main conclusion is that real expert-dependent power leakage exists, but a clean high-confidence per-expert fingerprint has not yet been achieved.