YOLOv8 for Video Surveillance: GPU Setup Guide

Why YOLOv8 for Video Surveillance

Modern security systems generate thousands of hours of video footage daily, but most of it goes unwatched. YOLOv8 transforms passive CCTV into an intelligent detection system that identifies people, vehicles, packages and anomalous behaviour in real time. Alerts trigger instantly when specified objects or events are detected, eliminating the need for constant human monitoring.

YOLOv8’s architecture processes video streams at high frame rates with exceptional accuracy, making it ideal for multi-camera deployments where latency matters. The model handles varying lighting conditions, angles and occlusion scenarios that traditional motion detection systems miss entirely.

Running YOLOv8 on dedicated GPU servers gives you the processing power to handle dozens of simultaneous camera feeds. A vision model hosting deployment means consistent frame rates, on-premises data processing and full control over detection sensitivity.

GPU Requirements for YOLOv8 Video Surveillance

The number of camera streams determines your GPU requirements. Below are tested configurations for YOLOv8 surveillance deployments. For detailed FPS data, see our YOLOv8 FPS by GPU benchmarks.

Check current availability on the vision model hosting page, or browse all options in our dedicated GPU hosting catalogue.

Quick Setup: Deploy YOLOv8 for Video Surveillance

Spin up a GigaGPU server, SSH in, and run the following to get YOLOv8 processing video streams. For GPU selection guidance, see our best GPU for YOLOv8 guide.

# Deploy YOLOv8 for real-time video surveillance
pip install ultralytics opencv-python-headless
python -c "
from ultralytics import YOLO
model = YOLO('yolov8m.pt')  # Medium model balances speed and accuracy
# Process RTSP camera stream
results = model.predict(
    source='rtsp://camera_ip:554/stream',
    stream=True, conf=0.5, classes=[0, 2, 7],  # person, car, truck
    save=False, show=False
)
for r in results:
    print(f'Detected {len(r.boxes)} objects')
"

This gives you a production-ready surveillance pipeline. For related security deployments, see YOLOv8 for Workplace Safety.

Performance Expectations

YOLOv8m processes 1080p video at approximately 85 FPS on an RTX 5090, far exceeding the 25-30 FPS typical of surveillance cameras. This headroom allows a single GPU to handle multiple concurrent streams with detection, tracking and alerting.

Actual results vary with model size, resolution and post-processing. Our FPS benchmark data and performance benchmarks provide detailed comparisons. For retail-specific detection, see YOLOv8 for Retail Analytics.

Cost Analysis

Commercial video analytics platforms charge per camera per month, typically £20-£100 per camera. A 32-camera deployment costs £640-£3,200 monthly. YOLOv8 on a single dedicated GPU handles the same camera count at a fraction of the cost, with far greater customisation.

With GigaGPU dedicated servers, you pay a flat monthly or hourly rate. An RTX 5090 server at £1.50-£4.00/hour handles 20-30 concurrent camera streams. Browse current rates on our GPU server pricing page.

For large-scale deployments with 64+ cameras, the RTX 6000 Pro tier provides the throughput and VRAM for simultaneous detection and tracking across all feeds. Visit our use cases and model guides for more deployment strategies.