Object detection at 115 FPS opens doors that slower cards simply cannot. We loaded Ultralytics YOLOv8m (11.2M parameters) onto an NVIDIA RTX 5080 (16 GB VRAM) and pushed it through our standard COCO-format test suite on a GigaGPU dedicated GPU server. The Blackwell-architecture 5080 punches well above its VRAM class for vision workloads — here is exactly what you get.
Raw Detection Throughput
| Metric | Value |
|---|---|
| FPS (640×640) | 115 FPS |
| Latency per frame | 8.7 ms |
| Precision | FP16 |
| Input resolution | 640×640 (COCO) |
| Performance rating | Excellent |
Benchmark conditions: FP16 inference, batch size 1, YOLOv8m model on COCO-format input at 640×640.
Memory Footprint
| Component | VRAM |
|---|---|
| Model weights (FP16) | 1.8 GB |
| Processing buffer | ~0.5 GB |
| Total RTX 5080 VRAM | 16 GB |
| Free headroom | ~14.2 GB |
YOLOv8m barely touches the 5080’s memory. That leftover 14.2 GB is not wasted — it means you can stack a secondary model alongside YOLO on the same card. Pair it with an LLM for scene narration, run PaddleOCR for license-plate reading, or add a tracking model. One GPU, multiple workloads, zero network hops between them.
What Does It Cost?
| Cost Metric | Value |
|---|---|
| Server cost | £0.95/hr (£189/mo) |
| Cost per 1M frames | £2.29 |
| Frames per £1 | 436681 |
At £2.29 per million frames, self-hosted detection on the RTX 5080 undercuts cloud vision APIs by an enormous margin. If your pipeline processes more than a few thousand frames daily, the savings add up fast. Check all benchmarks for cross-GPU comparisons.
Who Should Pick This Card
The RTX 5080 is the sweet spot for production YOLOv8 deployments that need high throughput without overspending. At 115 FPS you can comfortably serve multi-camera retail analytics, warehouse safety monitoring, or real-time drone feeds. The generous VRAM headroom also makes this card a natural fit for vision model hosting setups that combine detection with downstream reasoning.
Quick deploy:
docker run --gpus all -p 8080:8080 ultralytics/ultralytics:latest yolo detect predictSee our YOLOv8 hosting guide, best GPU for object detection, and all benchmark results. Related: LLaMA 3 8B on RTX 5080 benchmark.
Deploy YOLOv8 on RTX 5080
Order this exact configuration. UK datacenter, full root access.
Order RTX 5080 Server
