YOLOv8
Will it run on your hardware?
Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest
Need an exact figure for your context length? Use the VRAM calculator.
Run it locally
Copy-paste - running in under a minute
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Deep dive
Notes, sources, and the full write-up
YOLOv8
YOLOv8 by Ultralytics is the most popular open object detection model. It supports detection, segmentation, classification, pose estimation, and tracking - all in real time. With just 25M parameters for the base model, it runs on CPU.
Capabilities
| Task | Description |
|---|---|
| Detection | Bounding box object detection |
| Segmentation | Pixel-level instance segmentation |
| Classification | Image classification |
| Pose | Keypoint estimation |
| Tracking | Real-time object tracking |
| OBB | Oriented bounding boxes |
Quick start
from ultralytics import YOLO
# Load a model
model = YOLO("yolov8n.pt") # nano - smallest
# Detect objects
results = model("image.jpg")
# Show results
results[0].show()Model variants
| Variant | Params | Speed | Accuracy |
|---|---|---|---|
| Nano (n) | 3.2M | Fastest | Lowest |
| Small (s) | 11.2M | Fast | Good |
| Medium (m) | 25.9M | Balanced | Better |
| Large (l) | 43.7M | Slower | High |
| X-Large (x) | 68.2M | Slow | Highest |
When to use
- Security cameras - real-time person/vehicle detection
- Manufacturing - defect detection
- Retail - inventory tracking
- Robotics - environment perception
- Autonomous vehicles - obstacle detection
Frequently asked
Quick answers to common questions
How much VRAM does YOLOv8 need?
YOLOv8 with 0.03B parameters needs significant VRAM depending on quantization. Use our VRAM calculator for an exact estimate.
Is YOLOv8 better than other YOLO models?
YOLOv8 has 0.03B parameters with 8,192 context - a strong choice for object-detection, image-segmentation, pose-estimation.
What license is YOLOv8 under?
YOLOv8 is released under the AGPL-3.0 license, making it suitable for most commercial and personal projects.
What hardware runs YOLOv8 well?
With 0.03B parameters, YOLOv8 requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.
What is the best quantization for YOLOv8?
Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Step up to Q5_K_M or Q8_0 only if you have spare VRAM. Use our VRAM calculator to compare.
What models compete with YOLOv8?
YOLOv8 competes with other models in its class. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.
Compare & pair with
Similar models and recommended hardware
Nearby options
Similar models and compatible hardware by spec
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