Observed facts: the supplied review describes NM Card as containing 16 neural cores, five ARM cores, and 5 GB of memory. It also reports that the RZD “CHIZH” vision project replaced Jetson Nano with Modul hardware, found YOLOv4 unsupported, shifted to YOLOv5, and reached 30 fps for YOLOv5n at 320×320 after using four independent compute units in parallel.
The mechanism is architectural adaptation: incompatible model layers and a different precision/software stack force a team to select a supported network and partition inference across available compute units. This matters now because AI-hardware substitution is often framed as component procurement, while the deployment evidence points to application re-engineering as the actual unit of migration.
The first implication is that buyers should evaluate accelerator substitutions against a specific model graph, precision path, throughput target, and integration workload—not headline operations-per-second claims. The evidence boundary is that the reported measurements and deployment outcomes come from a single technical review and its cited project descriptions; they do not independently establish broad market leadership or equivalence with newer Jetson and Ascend products. The first watch condition is whether future public deployments publish reproducible model compatibility, latency, accuracy, power, and total-system cost results across multiple workloads.
No prior New Runtime record with usable content was supplied, so this starts a distinct analytical record focused on workload adaptation in industrial inference.