Vision Model Hosting
Deploy Computer Vision Models on Dedicated UK GPU Servers
Run YOLOv8, YOLOv9, PaddleOCR, EasyOCR, Detectron2, Segment Anything, CLIP, BLIP and OpenCV pipelines on private bare metal GPU servers. Ideal for OCR APIs, CCTV analytics, retail people counting, industrial inspection and document AI with no per-image billing.
What is Vision Model Hosting?
Vision model hosting means running computer vision workloads on your own dedicated GPU server instead of sending images or video frames to a third-party API.
With GigaGPU, you can host YOLOv8 and YOLOv9 detection APIs, PaddleOCR document pipelines, Segment Anything segmentation, CLIP and BLIP retrieval workloads, OpenCV video analytics, CCTV processing, retail people counting and document AI systems on private UK infrastructure with full root access.
This is ideal for teams that need vision model hosting with fixed monthly costs, lower latency, full control over frameworks like PyTorch, TensorFlow, OpenCV and Detectron2, plus easy expansion into multimodal model hosting or open source LLM hosting for document AI and image-aware assistants.
Built for private computer vision hosting, not shared-cloud image API queues.
Supported Vision Models
Run real computer vision stacks people actually deploy on dedicated GPUs — from YOLOv8 CCTV APIs and PaddleOCR document extraction to SAM segmentation, CLIP retrieval and OpenCV production pipelines.
Any Hugging Face-compatible computer vision model, OCR stack or OpenCV-based inference pipeline can be deployed. For OCR-heavy workflows, see PaddleOCR Hosting. For mixed image-plus-text systems, see Multimodal Model Hosting. If you need the infrastructure itself, see Dedicated GPU Hosting.
Best GPUs for Vision Model Hosting
Recommended GPUs for computer vision hosting, OCR workloads, object detection APIs and real-time video analytics.
A strong entry point for OCR hosting, light object detection APIs, low-resolution image classification and basic OpenCV inference at low monthly cost.
The sweet spot for many computer vision deployments. Great for real-time object detection, segmentation, OCR batching and production inference without enterprise pricing.
Ideal for heavier computer vision workloads, larger image batches, higher-resolution document pipelines and multi-stream processing at strong value.
Best for production-grade video analysis, multi-camera pipelines, high-resolution segmentation and more demanding real-time vision APIs with extra headroom.
Which GPU Do I Need for Computer Vision?
Answer three quick questions and get a recommended server for your vision AI workload.
Vision Model Hosting Pricing
Same GPU lineup, same live-price pattern, but positioned for computer vision, OCR, detection, segmentation and video analysis workloads.
Why Host Vision Models Instead of Using Google Vision API or AWS Rekognition?
If you need computer vision hosting at scale, dedicated GPU infrastructure gives you dramatically better cost control, full privacy and predictable performance compared to per-image API billing.
Per-Image API Providers
GigaGPU Dedicated Hosting
Dedicated GPU Hosting vs Vision APIs — The Real Cost
This is why teams searching for an alternative to Google Vision API or alternative to AWS Rekognition often move to dedicated GPU infrastructure once volume becomes sustained, privacy becomes important or custom vision pipelines are required.
Vision API vs Dedicated GPU — Cost Calculator
Estimate your monthly savings when switching from per-image API pricing to a dedicated GPU server for computer vision.
Vision Model Hosting — Real Workload Benchmarks
Benchmarks feel more believable when they map to the tools people actually deploy. Below we show estimated YOLOv8 FPS, PaddleOCR pages/sec and SAM latency by GPU for typical production-style workloads.
| GPU | VRAM | YOLOv8 FPS | PaddleOCR pages/sec | SAM latency | Best fit |
|---|---|---|---|---|---|
| RTX 3050 6GB | 6 GB | ~15 FPS | ~8 | ~420 ms/image | Entry OCR and testing |
| RTX 4060 8GB | 8 GB | ~30 FPS | ~15 | ~260 ms/image | Light YOLOv8 and OCR APIs |
| RTX 4060 Ti 16GB | 16 GB | ~45 FPS | ~22 | ~180 ms/image | Best value YOLOv8 + SAM starter |
| RTX 3090 24GB | 24 GB | ~60 FPS | ~35 | ~120 ms/image | PaddleOCR batching and multi-stream detection |
| RX 9070 XT 16GB | 16 GB | ~35 FPS | ~18 | ~210 ms/image | Cost-effective vision inference |
| Radeon AI Pro R9700 | 32 GB | ~50 FPS | ~28 | ~140 ms/image | High-VRAM OCR and segmentation |
| RTX 5080 16GB | 16 GB | ~65 FPS | ~38 | ~95 ms/image | Fast real-time YOLOv8 APIs |
| RTX 5090 32GB | 32 GB | ~120 FPS | ~60 | ~60 ms/image | Production CCTV, SAM and heavy video |
| RTX 6000 PRO 96GB | 96 GB | ~140+ FPS | ~70+ | ~45 ms/image | Enterprise multi-pipeline vision stacks |
YOLOv8 figures assume a 640×640 production-style detection pipeline. PaddleOCR is measured on A4 documents. SAM latency is a single-image estimate on typical segmentation workloads. Real-world performance varies with model size, batch size, preprocessing, TensorRT/ONNX optimisation and stream count. For adjacent stacks, see PaddleOCR Hosting, Multimodal Model Hosting and Dedicated GPU Hosting.
Vision Workload Suitability by GPU
A quick visual guide for choosing the right tier for YOLOv8, PaddleOCR, SAM and OpenCV production pipelines.
This graphic is a simplified buyer guide: RTX 4060-class GPUs are good for light OCR and detection, RTX 3090/5080-class GPUs suit production YOLOv8 and PaddleOCR, while RTX 5090 and RTX 6000 PRO are best for SAM, heavy video analytics and multi-pipeline deployments.
Computer Vision Hosting Use Cases
Dedicated GPU hosting for real vision products and production pipelines, not just demos.
OCR / Document Processing
Run private PaddleOCR and document AI pipelines for invoices, forms, contracts, scans and PDFs without per-page billing or sending documents to a third-party API.
ID / Passport Verification
Build internal identity verification flows using OCR, face matching and document parsing on private infrastructure with full control.
CCTV / Surveillance Analytics
Process live camera feeds with YOLOv8, YOLOv9 and OpenCV for detections, tracking, counting and event alerts with real-time inference and low-latency UK hosting.
Retail / People Counting
Deploy YOLOv8 and OpenCV tracking pipelines for store traffic analytics, queue monitoring and footfall measurement.
Autonomous Systems
Host detection and segmentation models for robotics, autonomous inspection systems and machine vision workloads that need dedicated performance.
AI Image Moderation
Run your own moderation stack for user uploads, marketplace images and content review without depending on external API providers.
Medical Imaging
Deploy segmentation and classification models for private healthcare imaging workflows where data control and predictable performance matter.
Industrial Inspection
Use SAM, Detectron2 and custom OpenCV pipelines for defect detection, quality control and production-line automation on dedicated GPU infrastructure.
Frameworks and Vision Stacks You Can Deploy
Build your own private computer vision platform with the tools you already use.
Deploy a Vision Model in 4 Steps
Go from order to private computer vision inference fast.
Choose the Right GPU
Pick a server based on image resolution, expected volume, FPS target, batch size and whether you are serving OCR, object detection or video analysis.
Provision the Server
Your dedicated GPU server is deployed with your chosen OS and full admin access so you can build exactly the vision stack you want.
Install Your Frameworks
Deploy PyTorch, TensorFlow, OpenCV, PaddleOCR, Ultralytics or your own custom inference pipeline. Add APIs, queues and preprocessing as needed.
Serve Your Own API
Expose internal or public image inference endpoints with predictable monthly pricing, private infrastructure and no shared-cloud image billing.
Vision Model Hosting — Frequently Asked Questions
Common questions about computer vision hosting, OCR hosting and self-hosted image AI infrastructure.
Deploy Your Vision Models on Dedicated GPU Infrastructure
Run OCR, object detection, segmentation and video analytics on private UK GPU servers with fixed monthly pricing, full root access and no per-image API billing.
