10 million random input vectors were used to validate the MATLAB models against AMD matrix hardware. That number does not prove production workload coverage, because the tested vectors are a randomized suite, not a training or inference corpus.

Buyers are constrained if they assume AMD matrix cores are interchangeable with NVIDIA tensor cores or with each other across CDNA generations. The abstract shows the numerical path is undocumented and device-specific, so software cannot force cross-device reproducibility. For a fab or buyer, that makes numerical behavior a procurement variable, not a footnote. MATLAB models are a numerical description, not a silicon replacement.

Throughput, power, latency, energy cost, and production workload coverage are not measured here. The two demonstrative applications only compare application-level accuracy differences, not deployment cost or silicon timing. No clock, node, or price appears in the abstract text here.

Buyers should treat AMD matrix-core numerics as a device-specific variable, not a portable IEEE 754 guarantee. The 10 million-vector match proves model fidelity on tested vectors, not production workload coverage.

The document does not measure throughput, power, latency, energy cost, or production workload coverage.

Engineers can use the MATLAB models to reproduce AMD matrix-core outputs on the tested vectors, but they cannot assume the same bit pattern across CDNA generations or production workloads.