What are the best techniques for reducing the size of Docker images containing Generative AI models without impacting performance during inference

Question

With the help of proper code example tell me What are the best techniques for reducing the size of Docker images containing Generative AI models without impacting performance during inference?

Answer 1

The best techniques for reducing Docker image size for Generative AI models include using minimal base images, multi-stage builds, model quantization, and pruning unnecessary dependencies while maintaining inference performance.

Here is the code snippet you can refer to:

In the above code we are using the following approaches:

• Uses a minimal python:3.9-slim base image to reduce unnecessary layers.
• Implements a multi-stage build to separate dependency installation from the final image.
• Avoids unnecessary files by using --no-cache-dir during pip install.
• Keeps the model lightweight by ensuring only essential files are included.

Hence, optimizing Docker images for Generative AI models requires minimal base images, multi-stage builds, dependency pruning, and model quantization while preserving inference efficiency.