Independent Edge-AI Bench
The GMKtec K16 runs the compile and test pipeline on native Ubuntu with real RAPL, thermal, and CPU-frequency telemetry. The Raspberry Pi 5 with full Hailo-8 runs isolated edge-load checks. Published numbers are measured, bounded, and labeled — nothing is claimed before it is recorded.
Stored hardware evidence from native-Ubuntu runs on the K16. Verified data only — every figure below is recorded bench output, not a projection. Hailo HEF compile + on-device load is now verified — see the gate result below.
16 normal/tuned intensity pairs tested on native Ubuntu. Green = recorded selective behavior (normal rejected all five cycles, tuned promoted). Amber = review case where the normal phase did not reject all five — marked honestly, not hidden. The tuned phase promoted in every case.
The bench hardware. Slots fill as each correct photo is placed with its verified caption.













The discipline is the product: unsupported paths fail early at a validation gate, verified results are publishable, the production Pi runtime is never modified, and the compile environment matches the Pi — never the reverse.
Written only after a verified event. No SEO filler, no untested fixes presented as solutions.
On native Ubuntu the K16 exposed RAPL power, thermal, and CPU-frequency telemetry, making it a real x86 hardware bench. The earlier WSL2 runs validated capacity and repeatability only — they are labeled non-native telemetry, not hardware-power evidence.
The native run rejected the normal/heavier condition across all five cycles at the efficiency gate and promoted the tuned/lighter condition only after repeatability cleared. A selective result, not always-on promotion.
Sixteen normal/tuned intensity pairs were tested; thirteen produced selective behavior, with the tuned phase promoting in every case. Shown as a boundary map, not a universal pass.
Under sustained load with six burn workers completed, the tuned condition was blocked by the safety gate. This is a safety-dominance negative control — deliberately not presented as a performance promotion.
yolov8s compiled to HEF on the K16 (Dataflow Compiler 3.30.0, Hailo-8), version-matched to the Pi 5 HailoRT runtime (firmware 4.20.0), then loaded on the Pi’s Hailo-8 through an isolated runtime check — separate from the production detector. Result: 185.15 FPS across 927 frames, int8, with zero version mismatch. This is a gate-calibration build (128-image calibration set, optimization level 1); production-accuracy recompilation and Frigate-detector integration are the next verified steps, not yet claimed.