AI Server for Computer Vision
GPU configurations for video analytics, image processing and specialised computer vision models.
What it is
CV workloads differ from LLMs: frame throughput, video decoding speed and data streaming matter. Such a server is designed around the scenario — number of cameras, model type, latency requirements — not just VRAM size.
When it fits
- Video analytics at sites and plants
- Production quality control
- Medical imaging and diagnostics
- OCR and document processing at scale
What to keep in mind
- CV and LLM loads have different profiles — one GPU rarely serves both well
- Video requires disk and network bandwidth, not just compute
- Real-time scenarios need different design than batch processing
- CV quality depends on data and capture conditions as much as on hardware
Frequent questions
Can one GPU serve both LLM and CV?+
Technically possible, often inefficient in practice. LLM load does not mix well with real-time video analytics. For serious scenarios we recommend separate GPUs.
How many cameras can one server handle?+
Depends on model, resolution, FPS and concurrent streams. Realistically 4 to 32 cameras per server. Precise sizing follows the specific CV scenario.
Is RTX 5090 suitable for CV?+
For development and small scenarios — yes. For production with multiple streams and real-time requirements, server-class GPUs (RTX PRO, L40S) are usually required.
Let’s discuss your task
Tell us what the AI contour needs to solve — we will match the configuration, infrastructure and next step.