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Home / Blog / Benchmarks / YOLOv8 on RTX 4060 Ti: Detection FPS & Cost, Category: Benchmarks, Slug: yolov8-on-rtx-4060-ti-benchmark, Excerpt: YOLOv8 benchmarked on RTX 4060 Ti: 56 FPS, VRAM usage, cost efficiency, and deployment configuration., Internal links: 8 –>
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YOLOv8 on RTX 4060 Ti: Detection FPS & Cost, Category: Benchmarks, Slug: yolov8-on-rtx-4060-ti-benchmark, Excerpt: YOLOv8 benchmarked on RTX 4060 Ti: 56 FPS, VRAM usage, cost efficiency, and deployment configuration., Internal links: 8 –>

YOLOv8 benchmarked on RTX 4060 Ti: 56 FPS, VRAM usage, cost efficiency, and deployment configuration.

56 FPS gives you a real option that few other mid-range cards offer: processing two full 25 FPS video streams simultaneously on a single GPU. The RTX 4060 Ti turns YOLOv8 from a single-camera tool into a genuine multi-feed detection server. We validated the numbers on GigaGPU.

Detection Throughput

MetricValue
FPS (640×640)56 FPS
Latency per frame17.9 ms
PrecisionFP16
Input resolution640×640 (COCO)
Performance ratingVery Good

Benchmark conditions: FP16 inference, batch size 1, YOLOv8m model on COCO-format input at 640×640.

VRAM: 14 GB Free

ComponentVRAM
Model weights (FP16)1.8 GB
Processing buffer~0.5 GB
Total RTX 4060 Ti VRAM16 GB
Free headroom~14.2 GB

Fourteen GB of headroom with YOLOv8 running is almost comical. You could co-host PaddleOCR, a Whisper instance for audio-visual monitoring, and still have VRAM left for a tracking model. The 4060 Ti is not just a detection card — it is a full computer vision server waiting to be utilised.

Cost Per Million Frames

Cost MetricValue
Server cost£0.50/hr (£99/mo)
Cost per 1M frames£2.48
Frames per £1403,226

At £2.48 per million frames, the 4060 Ti is nearly identical to the RTX 4060 on cost-per-frame while delivering 33% more throughput. For deployments where you need to squeeze the maximum detection rate per pound, this card sits at the sweet spot. Full data on the benchmark dashboard.

Multi-Camera Reality

The maths works out cleanly: two cameras at 25 FPS each equals 50 FPS needed, and the 4060 Ti delivers 56. In real-world deployments, account for pre-processing overhead and you comfortably handle two feeds with occasional peaks. For three or more cameras, the RTX 3090 at 78 FPS gives you the margin you need. Planning details: best GPU for YOLO.

Quick deploy:

docker run --gpus all -p 8080:8080 ultralytics/ultralytics:latest yolo detect predict

See also: YOLOv8 hosting guide, all benchmarks, Coqui TTS hosting.

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