Published: 2025-05-06 | English reference version of the Chinese source article.
Why server selection follows the workload
A local AI server can support multi-channel video analytics, model training, private deployment of an AI model or a combined project workload. The processor, accelerator, memory, storage, network and operating environment are chosen against the actual applications rather than as a generic compute specification.
XINHUO AI publishes configurable domestic-compute options that can use Huawei Kunpeng and Ascend, Phytium, Hygon, MetaX and related hardware. The final design needs the expected number of video streams, model family, inference or training workload, data-retention plan and external platform interfaces.
Video analytics needs more than an accelerator
A video analytics server decodes streams, schedules inference, applies rules, retains event evidence and communicates with a platform. CPU capacity, GPU or NPU selection, system memory, storage throughput and network throughput all affect sustained use.
A customer should run planned channels and algorithms together before purchase acceptance. The test should include peak target density, preview, capture storage, event pushes, stream reconnection and the platform's own receiving behaviour.
How it works with the XINHUO AI product line
The server can sit alongside AI cameras and AI Edge Boxes. Cameras and edge devices handle distributed or existing video points. The server is used where many streams or a larger local model workload must be managed in one place. XINHUOAI can be used to prepare and export a custom vision model for the selected hardware path.
A project should document the delivered model, software build, enabled algorithms, stream plan and interface map. That record is needed when the site adds channels or changes its camera conditions.