Can I load 2 or more models into 1 GPU for inference if I have enough GPU memory?

[darouwan]

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Question

Currently I load one YOLOv8 model into one GPU(Tesla P4) for inference. But the model only costs 600MB memory and my GPU has 8GB in total, and most of time the GPU is in idle. So can I load multiple models into my single GPU currently to provide inference service? Does it have any risks?

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[glenn-jocher]

@darouwan yes, you can load multiple YOLO models into a single GPU for inference, provided you have sufficient GPU memory. This can help you utilize your GPU resources more efficiently. Here are a few considerations and steps to ensure smooth operation:

1. Memory Management: Ensure that the combined memory usage of all models does not exceed the available GPU memory. You can monitor GPU memory usage using tools like nvidia-smi.

2. Thread Safety: When running multiple models concurrently, it's crucial to manage thread safety. Each model should be instantiated within its own thread to avoid race conditions. Here’s a thread-safe example:

   from threading import Thread
   from ultralytics import YOLO
   
   def thread_safe_predict(model_path, image_path):
       model = YOLO(model_path)
       results = model.predict(image_path)
       # Process results
   
   # Starting threads with different models
   Thread(target=thread_safe_predict, args=("yolov8n.pt", "image1.jpg")).start()
   Thread(target=thread_safe_predict, args=("yolov8s.pt", "image2.jpg")).start()

3. Concurrency: If you are running inference in a multi-threaded environment, ensure that each thread has its own model instance. This prevents any potential conflicts and ensures thread safety.

4. Performance: Loading multiple models can increase the inference time due to context switching and resource sharing. It's a good idea to benchmark and profile your application to understand the performance implications.

5. Latest Versions: Make sure you are using the latest versions of the Ultralytics packages to benefit from the latest optimizations and bug fixes.

If you encounter any issues or need further assistance, please provide a minimum reproducible example as outlined here. This will help us diagnose and address any problems more effectively.

Feel free to experiment with these suggestions, and let us know if you have any further questions! 😊