Published: 2025-03-27 | English reference version of the Chinese source article.
One server can have several roles, but the workload must be clear
GPU servers are used for several different jobs: multi-channel video analytics, training a vision model, operating a local large model, or serving a project platform. These jobs have different memory, storage, network and software requirements. A specification should name the workload before it names the accelerator.
XINHUO AI provides configurable NVIDIA and Huawei Ascend server options for these local workloads. The selection is based on the planned camera channels, algorithms per channel, video quality, model size, training need, integration tasks and data policy.
Video analytics and multimodal review can share a project architecture
A GPU server can run centralized inference for many camera streams. It can also host a local multimodal model for reviewing selected low-confidence or complex event evidence where the project design supports it. Continuous first-pass video detection remains a separate workload and must be sized accordingly.
The project team should decide which events are sent for a second review, what evidence is retained, how the review result is displayed and whether the decision remains subject to operator confirmation. This prevents a large model from becoming an uncontrolled bottleneck in a live video system.
A delivered system must be measured as a whole
A server that starts a model is not automatically ready for a site. The test should cover real camera streams, live preview, event capture, message delivery, recovery after faults, the customer platform and the time the operator needs to find the related evidence.
For custom vision tasks, XINHUOAI can support material organization, GPU training, evaluation and model export. The finished model still needs to run on the planned hardware with the real video and rule configuration.