RTX 5060 Ti 16GB for Computer Vision

Computer vision workloads – object detection, segmentation, image embedding, multi-camera analytics – scale almost linearly with GPU throughput, and the RTX 5060 Ti 16GB is a sweet spot for the class. Blackwell’s 5th-gen tensor cores, 448 GB/s memory bandwidth and native INT8/FP8 give it roughly 1,450 FPS on YOLOv8 nano via TensorRT and enough VRAM for 30+ concurrent HD camera streams. Here’s what that looks like in production on a Gigagpu UK dedicated node.

Contents

• YOLOv8 throughput
• Multi-stream capacity
• CLIP and image embeddings
• Segmentation and pose
• Deployment notes
• Choosing a stack

YOLOv8 throughput

YOLOv8 is still the default real-time detector for most production CV systems. Numbers below are 640×640 input, FP16 weights, single GPU, measured with batch=1 for latency and batch=8 for throughput.

Multi-stream capacity

In practice, a GPU serving CCTV/analytics traffic is bound by concurrent decoded frames rather than model FPS alone. NVDEC on Blackwell handles 8 simultaneous 1080p30 streams per engine, and with batched inference the card comfortably covers 30+ HD cameras for realtime detection.

A 30-camera 1080p25 deployment with YOLOv8s INT8 uses around 15% of the card’s compute, which leaves significant headroom for downstream tasks: tracking (ByteTrack, BoT-SORT), ReID embeddings, license-plate OCR.

CLIP and image embeddings

For visual search, duplicate detection and content moderation, CLIP-based embeddings are the workhorse. On the 5060 Ti:

4,200 images/second on ViT-B/32 translates to 15 million images/hour, which is enough to embed Unsplash-scale datasets in an afternoon on a single card.

Segmentation and pose

• YOLOv8n-seg – 540 FPS PyTorch, 980 FPS TensorRT FP16.
• YOLOv8m-seg – 230 FPS PyTorch, 420 FPS TensorRT.
• SAM2 (hiera-b+) – 42 FPS on 1024×1024 mask prediction.
• YOLOv8n-pose – 680 FPS PyTorch, 1,250 FPS TensorRT.
• RT-DETR-L – 195 FPS TensorRT at 640×640.

Deployment notes

# Export YOLOv8s to TensorRT INT8
yolo export model=yolov8s.pt format=engine device=0 \
  half=False int8=True data=coco.yaml workspace=4

# Triton with TensorRT backend, dynamic batching
tritonserver --model-repository=/models --strict-model-config=false \
  --log-verbose=1

Choosing a stack

• < 10 HD streams -> YOLOv8s/m PyTorch is fine, easiest to operate.
• 10-30 HD streams -> YOLOv8s TensorRT FP16/INT8 + Triton dynamic batching.
• 30+ streams or < 1 ms latency -> YOLOv8n INT8, batched.
• Visual search -> CLIP ViT-B/16 or SigLIP in FP16.
• Medical / industrial segmentation -> SAM2 + domain fine-tunes.

See also: YOLOv8 benchmark, PaddleOCR benchmark, embedding throughput, Qwen VL benchmark, Llama 3.2 Vision benchmark.