Estimate the AI computing power required for your intelligent surveillance system. Select your AI tasks, camera count, and resolution to determine the GPU/NPU requirements for your platform.
Compare common AI hardware platforms for video surveillance applications. TOPS (Tera Operations Per Second) is the standard measure of AI inference performance.
| Hardware Platform | Performance (TOPS) | Power (W) | Typical Channels | Best For |
|---|---|---|---|---|
| Edge AI Camera (built-in) | 0.5–4 TOPS | 5–15W | 1 | Single-camera AI, basic detection |
| Embedded AI Box (NPU) | 4–16 TOPS | 10–30W | 4–8 | Small deployments, edge inference |
| Mid-range AI Server (GPU) | 50–200 TOPS | 100–300W | 16–64 | Medium deployments, multi-task AI |
| High-end AI Server (GPU) | 200–1000 TOPS | 300–800W | 64–256 | Large deployments, complex AI tasks |
| AI Server Cluster | 1000+ TOPS | 1–10kW | 256+ | City-scale, smart city platforms |
Edge AI cameras process locally, reducing bandwidth and latency. Central AI servers offer more flexibility and can handle complex multi-camera analytics. A hybrid approach is often optimal.
AI models are continuously improving. Always provision 30–50% extra compute capacity for model updates, new AI tasks, and peak load periods.
For most surveillance AI tasks, 5–10 fps analysis provides excellent accuracy. Real-time 25fps analysis requires 5× more compute than 5fps with minimal accuracy improvement.
Quantized AI models (INT8) can achieve 2–4× better performance than FP32 models with minimal accuracy loss. Use hardware-optimized models for your specific NPU/GPU platform.
AI servers generate significant heat. Plan for adequate cooling (typically 1.2–1.5× power consumption in cooling capacity) and ensure stable power supply with UPS backup.
Design your AI platform to scale horizontally. Use containerized AI inference (Docker/Kubernetes) to easily add compute nodes as your camera count grows.
Our AI surveillance engineers will design the complete intelligent video analytics platform for your project, including hardware selection, model deployment, and integration planning.